"""Create a Bayes Net representation of the above power plant problem. CS 344 and CS 386 are core courses in the CSE undergraduate programme. # For the main exercise, consider the following scenario: # There are five frisbee teams (T1, T2, T3,...,T5). Assignments 3-6 don't get any easier. Admission Criteria; Application Deadlines, Process and Requirements; FAQ; Current Students. Be sure to include your name and student number as a comment in all submitted documents. However, the alarm is sometimes faulty, and the gauge is more likely to fail when the temperature is high. Don't worry about the probabilities for now. You can access these by calling : # A.dist.table, AvB.dist.table :Returns the same numpy array that you provided when constructing the probability distribution. Assignment 3: Bayesian Networks, Inference and Learning CS486/686 – Winter 2020 Out: February 20, 2020 Due: March 11, 2020 at 5pm Submit your assignment via LEARN (CS486 site) in the Assignment 3 … § Bayes’ nets implicitly encode joint distribu+ons § As a product of local condi+onal distribu+ons § To see what probability a BN gives to a full assignment, mul+ply all the relevant condi+onals together: Example: Alarm Network Burglary Earthqk Alarm John calls Mary calls B P(B) +b 0.001 … Date handed out: May 25, 2012 Date due: June 4, 2012 at the start of class Total: 30 points. # If you need to sanity-check to make sure you're doing inference correctly, you can run inference on one of the probabilities that we gave you in 1c. 10-601 Machine Learning, Fall 2011: Homework 3 Machine Learning Department Carnegie Mellon University Due: October 17, 5 PM Instructions There are 3 questions on this assignment. # Design a Bayesian network for this system, using pbnt to represent the nodes and conditional probability arcs connecting nodes. # We want to ESTIMATE the outcome of the last match (T5vsT1), given prior knowledge of other 4 matches. Git is a distributed version control system that makes it easy to keep backups of different versions of your code and track changes that are made to it. Submit your homework as 3 separate sets of pages, Why or why not? ## CS 6601 Assignment 3: Bayes Nets In this assignment, you will work with probabilistic models known as Bayesian networks to efficiently calculate the answer to probability questions concerning discrete random variables. Lab Assignment 3 (10 marks). 1 [20 Points] Short Questions 1.1 True or False (Grading: Carl Doersch) Answer each of the following True of … And return the likelihoods for the last match. A match is played between teams Ti and Ti+1 to give a total of 5 matches, i.e. In it, I discuss what I have learned throughout the course, my activities and findings, how I think I did, and what impact it had on me. January 31: Lab Assignment 4 (10 marks). """Multiple choice question about polytrees. # 5. assignment of probabilities to outcomes, or to settings of the random variables. # Estimate the likelihood of different outcomes for the 5 match (T5vT1) by running Gibbs sampling until it converges to a stationary distribution. The course gives an good overview of the different key areas within AI. CS6601 Project 2. random.randint()) for the probabilistic choices that sampling makes. The latter is a former Google Search Director who also guest lectures on Search and Bayes Nets. # Suppose that you know the following outcome of two of the three games: A beats B and A draws with C. Start by calculating the posterior distribution for the outcome of the BvC match in calculate_posterior(). But, we’ve also learned that this is only generally feasible in Bayes nets that are singly connected. You'll do this in MH_sampling(), which takes a Bayesian network and initial state as a parameter and returns a sample state drawn from the network's distribution. You can always update your selection by clicking Cookie Preferences at the bottom of the page. Having taken Knowledge Based AI (CS 7637), AI for Robotics (CS 8803-001), Machine Learning (CS 7641) and Reinforcement Learning (CS 8803-003) before, I must say that the AI course syllabus had… About me I am a … We use analytics cookies to understand how you use our websites so we can make them better, e.g. # Rather than using inference, we will do so by sampling the network using two [Markov Chain Monte Carlo](http://www.statistics.com/papers/LESSON1_Notes_MCMC.pdf) models: Gibbs sampling (2c) and Metropolis - Hastings sampling (3a). # Build a Bayes Net to represent the three teams and their influences on the match outcomes. This assignment is about using the Markov Chain Monte Carlo technique (also known as Gibbs Sampling) for approximate inference in Bayes nets. Why OMS CS? Reading: Pieter Abbeel's introduction to Bayes Nets. This page constitutes my learning portfolio for CS 6601, Artificial Intelligence, taken in Fall 2012. Base class for a Bayes Network classifier. For more information, see our Privacy Statement. ## CS 6601 Assignment 3: Bayes Nets In this assignment, you will work with probabilistic models known as Bayesian networks to efficiently calculate the answer to probability questions concerning discrete random variables. For simplicity, say that the gauge's "true" value corresponds with its "hot" reading and "false" with its "normal" reading, so the gauge would have a 95% chance of returning "true" when the temperature is hot and it is not faulty. # Using pbnt's Distribution class: if you wanted to set the distribution for P(A) to 70% true, 30% false, you would invoke the following commands. """Complete a single iteration of the Gibbs sampling algorithm. The alarm is faulty 15% of the time. If you have technical difficulties submitting the assignment to Canvas, post privately to Piazza immediately and attach your submission. First, take a look at bayesNet.py to see the classes you'll be working with - BayesNet and Factor.You can also run this file to see an example BayesNet and associated Factors:. Please submit your completed homework to Sharon Cavlovich (GHC 8215) by 5pm, Monday, October 17. Returns the new state sampled from the probability distribution as a tuple of length 10. # Hint : Checkout ExampleModels.py under pbnt/combined. """Complete a single iteration of the MH sampling algorithm given a Bayesian network and an initial state value. For instance, running inference on $P(T=true)$ should return 0.19999994 (i.e. # Here's an example of how to do inference for the marginal probability of the "faulty alarm" node being True (assuming "bayes_net" is your network): # F_A = bayes_net.get_node_by_name('faulty alarm'), # engine = JunctionTreeEngine(bayes_net), # index = Q.generate_index([True],range(Q.nDims)). … In it, I discuss what I have learned throughout the course, my activities and findings, how I think I did, and what impact it had on me. # For the first sub-part, consider a smaller network with 3 teams : the Airheads, the Buffoons, and the Clods (A, B and C for short). Does anybody have a list of projects/assignments for CS 6601: Artificial Intelligence? Resources Udacity Videos: Lecture 5 on Probability Lecture 6 on Bayes Nets Textbook Chapters: 13 Quantifying … # Hint : Checkout example_inference.py under pbnt/combined, """Set probability distribution for each node in the power plant system. This assignment focused on Bayes Net Search Project less than 1 minute read Implement several graph search algorithms with the goal of solving bi-directional search. Against this context, I was interested to know how a top CS and Engineering college taught AI. You'll do this in Gibbs_sampling(), which takes a Bayesian network and initial state value as a parameter and returns a sample state drawn from the network's distribution. 15-381 Spring 06 Assignment 6 Solution: Neural Nets, Cross-Validation and Bayes Nets Questions to Sajid Siddiqi (siddiqi@cs.cmu.edu) Out: 4/17/06 Due: 5/02/06 Name: Andrew ID: Please turn in your answers on this assignment (extra copies can be obtained from the class web page). For simplicity, we assume that the temperature is represented as either high or normal. ### Resources You will find the following resources helpful for this assignment. The method should just perform a single iteration of the algorithm. First, work on a similar, smaller network! python bayesNet.py. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. CS 188: Artificial Intelligence Bayes’ Nets: Independence Instructors: ... §Bayes’nets implicitly encode joint distributions §As a product of local conditional distributions §To see what probability a BN gives to a full assignment, multiply all the relevant conditionals together: Example: Alarm Network B P(B) +b 0.001 No description, website, or topics provided. The key is to remember that 0 represents the index of the false probability, and 1 represents true. and facilities common to Bayes Network learning algorithms like K2 and B. Assignment 2: Map Search leveraging breadth-first, uniform cost, a-star, bidirectional a-star, and tridirectional a-star. T1vsT2, T2vsT3,...,T4vsT5,T5vsT1. This page constitutes my external learning portfolio for CS 6601, Artificial Intelligence, taken in Spring 2012. GitHub is a popular web hosting service for Git repositories. # The general idea is to build an approximation of a latent probability distribution by repeatedly generating a "candidate" value for each random variable in the system, and then probabilistically accepting or rejecting the candidate value based on an underlying acceptance function. Check Hints 1 and 2 below, for more details. # A_distribution = DiscreteDistribution(A), # index = A_distribution.generate_index([],[]), # If you wanted to set the distribution for P(A|G) to be, # dist = zeros([G_node.size(), A.size()], dtype=float32), # A_distribution = ConditionalDiscreteDistribution(nodes=[G_node,A], table=dist), # Modeling a three-variable relationship is a bit trickier. 3 Bayes’ Nets ! Assignment 4: Continuous Decision Trees and Random Forests """Compare Gibbs and Metropolis-Hastings sampling by calculating how long it takes for each method to converge, """Question about sampling performance. Assignment 3: Bayes Nets. Otherwise, the gauge is faulty 5% of the time. CS 188: Artificial Intelligence Bayes’ Nets Instructor: Anca Dragan ---University of California, Berkeley [These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. 15-381 Spring 06 Assignment 6 Solution: Neural Nets, Cross-Validation and Bayes Nets Questions to Sajid Siddiqi (siddiqi@cs.cmu.edu) Out: 4/17/06 Due: 5/02/06 Name: Andrew ID: Please turn in your answers on this assignment (extra copies can be obtained from the class web page). Home; Prospective Students. This is a collection of assignments from OMSCS 6601 - Artificial Intelligence. Fill out the function below to create the net. WRITE YOUR CODE BELOW. 3 total matches are played. Use EnumerationEngine ONLY. First, take a look at bayesNet.py to see the classes you'll be working with - BayesNet and Factor.You can also run this file to see an example BayesNet and associated Factors:. Answer true or false for the following questions on d-separation. GitHub is where the world builds software. # "YOU WILL SCORE 0 POINTS ON THIS ASSIGNMENT IF YOU USE THE GIVEN INFERENCE ENGINES FOR THIS PART!! GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. There are also plenty of online courses on “How to do AI in 3 hours” (okay maybe I’m exaggerating a bit, it’s How to do AI in 5 hours). """Create a Bayes Net representation of the game problem. # 2a: Build a small network with for 3 teams. Learn more, Code navigation not available for this commit, Cannot retrieve contributors at this time, """Testing pbnt. 8 Definition • A Bayes’ Net is a directed, acyclic graph Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. March 21: Class Test 3, Probabilistic reasoning. You can always update your selection by clicking Cookie Preferences at the bottom of the page. """, # ('The marginal probability of sprinkler=false:', 0.80102921), #('The marginal probability of wetgrass=false | cloudy=False, rain=True:', 0.055). Bayes' Nets and Factors. DO NOT CHANGE ANY FUNCTION HEADERS FROM THE NOTEBOOK. For instance, if Metropolis-Hastings takes twice as many iterations to converge as Gibbs sampling, you'd say that it converged faster by a factor of 2. # Knowing these facts, set the conditional probabilities for the necessary variables on the network you just built. Problem. For instance, when it is faulty, the alarm sounds 55% of the time that the gauge is "hot" and remains silent 55% of the time that the gauge is "normal.". 2/14/2018 omscs6601/assignment_3 1/7 CS 6601 Assignment 3: Probabilistic Modeling In this assignment, you will work with probabilistic models known as Bayesian networks to efficiently calculate the answer to probability questions concerning discrete random variables. Informal first introduction of Bayes’ nets through causality “intuition” ! By approximately what factor? """. Homework Assignment #4: Bayes Nets Solution Silent Policy: A silent policy will take effect 24 hours before this assignment is due, i.e. For example, to connect the alarm and temperature nodes that you've already made (i.e. # 4. Assignment 1 - Isolation Game - CS 6601: Artificial Intelligence Probabilistic Modeling less than 1 minute read CS6601 Assignment 3 - OMSCS. ### Resources You will find the following resources helpful for this assignment. Test your implementation by placing this file in the same directory as your propagators.py and sudoku_csp.py files containing your implementation, and then execute python3 student_test_a2.py Or if the default python on your system is already python3 you … The temperature gauge reads the correct temperature with 95% probability when it is not faulty and 20% probability when it is faulty. The written portion of this assignment is to be done individually. Lab Assignment 3 (10 marks). About me I am a … # Hint 3: you'll also want to use the random package (e.g. Be sure to include your name and student number as a comment in all submitted documents. Use the following Boolean variables in your implementation: # - G = gauge reading (high = True, normal = False), # - T = actual temperature (high = True, normal = False). # The following command will create a BayesNode with 2 values, an id of 0 and the name "alarm": # NOTE: Do not use any special characters(like $,_,-) for the name parameter, spaces are ok. # You will use BayesNode.add\_parent() and BayesNode.add\_child() to connect nodes. Thus, the independence expressed in this Bayesian net are that A and B are (absolutely) independent. I recently completed the Artificial Intelligence course (CS 6601) as part of OMSCS Fall 2017. they're used to log you in. – Example : P(H=y, F=y) = 2/8 • Could encode this into a table: ... • Bayes’ nets can solve this problem by exploiting independencies. This is a collection of assignments from OMSCS 6601 - Artificial Intelligence, Isolation game using minimax algorithm, and alpha-beta, Map Search leveraging breadth-first, uniform cost, a-star, bidirectional a-star, and tridirectional a-star, Continuous Decision Trees and Random Forests. Conditional Independences ! – Example : P(H=y, F=y) = 2/8 In it, I discuss what I have learned throughout the course, my activities and findings, how I think I did, and what impact it had on me. # TODO: write an expression for complexity. You signed in with another tab or window. We use essential cookies to perform essential website functions, e.g. One way to do this is by returning the sample as a tuple. CS 188: Artificial Intelligence Bayes’ Nets: Sampling Instructors: Dan Klein and Pieter Abbeel --- University of California, Berkeley [These slides were created by Dan … For example, write 'O(n^2)' for second-degree polynomial runtime. This assignment will be graded on the accuracy of the functions you completed. CS 188: Artificial Intelligence Bayes’ Nets: Sampling Instructor: Professor Dragan --- University of California, Berkeley [These slides were created by Dan Klein and … Assignment 3 deals with Bayes nets, 4 is decision trees, 5 is expectimax and K-means, 6 is hidden Markov models (6 was a bit easier IMO). I enjoyed the class, but it is definitely a time sink. """, # If an initial value is not given, default to a state chosen uniformly at random from the possible states, # print "Randomized initial state: ", initial_value, # Update skill variable based on conditional joint probabilities, # skill_prob_num = team_table[initial_value[x]] * match_table[initial_value[x], initial_value[(x+1)%n], initial_value[x+n]] * match_table[initial_value[(x-1)%n], initial_value[x], initial_value[(x+(2*n)-1)%(2*n)]], # Update game result variable based on parent skills and match probabilities. For more information, see our Privacy Statement. UPDATED student_test_a2.py This is the tester script. Learning Bayes’ Nets from Data 5 Graphical Model Notation ! Test the MCMC algorithm on a number of Bayes nets, including one of your own creation. C is independent of B given A. # Hint 2: To use the AvB.dist.table (needed for joint probability calculations), you could do something like: # p = match_table[initial_value[x-n],initial_value[(x+1-n)%n],initial_value[x]], where n = 5 and x = 5,6,..,9. I enjoyed the class, but it is definitely a time sink. they're used to log you in. If you wanted to set the following distribution for $P(A|G,T)$ to be, # dist = zeros([G_node.size(), T_node.size(), A.size()], dtype=float32), # A_distribution = ConditionalDiscreteDistribution(nodes=[G_node, T_node, A], table=dist). ', 'Yes, because its underlying undirected graph is a tree. assuming that temperature affects the alarm probability): # You can run probability\_tests.network\_setup\_test() to make sure your network is set up correctly. You can also calculate the answers by hand to double-check. ## CS 6601 Assignment 3: Bayes Nets In this assignment, you will work with probabilistic models known as Bayesian networks to efficiently calculate the answer to probability questions concerning discrete random variables. CS 188: Artificial Intelligence Spring 2010 Lecture 15: Bayes’ Nets II – Independence 3/9/2010 Pieter Abbeel – UC Berkeley Many slides over the course adapted from Dan Klein, Stuart Russell, Andrew Moore Announcements Current readings Require login Assignments W4 due Thursday Midterm 3/18, 6-9pm, 0010 Evans --- no lecture on 3/18 Although be careful while indexing them. assignment, taking advantage of the policy only in an emergency. The alarm responds correctly to the gauge 55% of the time when the alarm is faulty, and it responds correctly to the gauge 90% of the time when the alarm is not faulty. 1 Home; Prospective Students. If nothing happens, download Xcode and try again. You signed in with another tab or window. Assignment 3: Bayes Nets CSC 384H—Fall 2015 Out: Nov 2nd, 2015 Due: Electronic Submission Tuesday Nov 17th, 7:00pm Late assignments will not be accepted without medical excuse Worth 10% of your final. This page constitutes my external learning portfolio for CS 6601, Artificial Intelligence, taken in Spring 2012. You should look at the printStarterBayesNet function - there are helpful comments that can make your life much easier later on.. You can just use the probability distributions tables from the previous part. # You're done! (Make sure to identify what makes it different from Metropolis-Hastings.). ## CS 6601 Assignment 3: Bayes Nets In this assignment, you will work with probabilistic models known as Bayesian networks to efficiently calculate the answer to probability questions concerning discrete random variables. # # Update skill variable based on conditional joint probabilities, # skill_prob[i] = team_table[i] * match_table[i, initial_value[(x+1)%n], initial_value[x+n]] * match_table[initial_value[(x-1)%n], i, initial_value[(2*n-1) if x==0 else (x+n-1)]], # skill_prob = skill_prob / normalize, # initial_value[x] = np.random.choice(4, p=skill_prob), # # Update game result variable based on parent skills and match probabilities, # result_prob = match_table[initial_value[x-n], initial_value[(x+1-n)%n], :], # initial_value[x] = np.random.choice(3, p=result_prob), # current_weight = A.dist.table[initial_value[0]]*A.dist.table[initial_value[1]]*A.dist.table[initial_value[2]] \, # *AvB.dist.table[initial_value[0]][initial_value[1]][initial_value[3]]\, # *AvB.dist.table[initial_value[1]][initial_value[2]][initial_value[4]]\, # *AvB.dist.table[initial_value[2]][initial_value[0]][initial_value[5]], # new_weight = A.dist.table[new_state[0]]*A.dist.table[new_state[1]]*A.dist.table[new_state[2]] \, # *AvB.dist.table[new_state[0]][new_state[1]][new_state[3]]\, # *AvB.dist.table[new_state[1]][new_state[2]][new_state[4]]\, # *AvB.dist.table[new_state[2]][new_state[0]][new_state[5]], # arbitrary initial state for the game system. Assume the following variable conventions: # |AvB | the outcome of A vs. B
(0 = A wins, 1 = B wins, 2 = tie)|, # |BvC | the outcome of B vs. C
(0 = B wins, 1 = C wins, 2 = tie)|, # |CvA | the outcome of C vs. A
(0 = C wins, 1 = A wins, 2 = tie)|. Assignment 1 - Isolation Game - CS 6601: Artificial Intelligence Probabilistic Modeling less than 1 minute read CS6601 Assignment 3 - OMSCS. Assignment 1: Isolation game using minimax algorithm, and alpha-beta. Each team has a fixed but unknown skill level, represented as an integer from 0 to 3. ### Resources You will find the following resources helpful for this assignment. they're used to gather information about the pages you visit … Assignments 3-6 don't get any easier. If an initial value is not given, default to a state chosen uniformly at random from the possible states. Why OMS CS? • A tool for reasoning probabilistically. You'll be using GitHub to host your assignment code. # 1d: Probability calculations : Perform inference. The main components of the assignment are the following: Implement the MCMC algorithm. of the BvC match given that A won against, B and tied C. Return a list of probabilities, corresponding to win, loss and tie likelihood. Choose from the following answers. """, sampling by calculating how long it takes, #return Gibbs_convergence, MH_convergence. With just 3 teams (Part 2a, 2b). Creating a Bayes Net 1.Choose a set of relevant variables 2.Choose an ordering of them, call them X 1, …, X N 3.for i= 1 to N: 1.Add node X ito the graph 2.Set parents(X i) to be the minimal subset of {X 1…X i-1}, such that x iis conditionally independent of all other members of {X 1…X i-1} given parents(X i) 3… These [slides](https://www.cs.cmu.edu/~scohen/psnlp-lecture6.pdf) provide a nice intro, and this [cheat sheet](http://www.bcs.rochester.edu/people/robbie/jacobslab/cheat_sheet/MetropolisHastingsSampling.pdf) provides an explanation of the details. # "YOU WILL SCORE 0 POINTS IF YOU USE THE GIVEN INFERENCE ENGINES FOR THIS PART!!". they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. If an initial value is not given, default to a state chosen uniformly at random from the possible states. The method should just consist of a single iteration of the algorithm. Run this before anything else to get pbnt to work! Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. Representation ! I completed the Machine Learning for Trading (CS 7647-O01) course during the Summer of 2018.This was a fun and light course. CS 188: Artificial Intelligence Bayes’ Nets: Independence Instructors: Pieter Abbeel & Dan Klein ---University of California, Berkeley [These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. download the GitHub extension for Visual Studio. Please hand in a hardcopy. Bayes’ Nets Dan Klein CS121 Winter 2000-2001 2 What are they? # Assume that each team has the following prior distribution of skill levels: # In addition, assume that the differences in skill levels correspond to the following probabilities of winning: # | skill difference
(T2 - T1) | T1 wins | T2 wins| Tie |, # |------------|----------|---|:--------:|. # arbitrary initial state for the game system : # 5 for matches T1vT2,T2vT3,....,T4vT5,T5vT1. You can check your probability distributions with probability_tests.probability_setup_test(). Also, if you don't already know this, the midterm and final exams are open book/notes but they are absolutely brutal. This page constitutes my exernal learning portfolio for CS 6601, Artificial Intelligence, taken in Spring 2012. 10-601 Machine Learning, Fall 2011: Homework 3 Machine Learning Department Carnegie Mellon University Due: October 17, 5 PM Instructions There are 3 questions on this assignment. If nothing happens, download the GitHub extension for Visual Studio and try again. Learn more. CS 188: Artificial Intelligence Bayes’ Nets: Independence Instructors: Pieter Abbeel & Dan Klein ---University of California, Berkeley [These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. We'll say that the sampler has converged when, for 10 successive iterations, the difference in expected outcome for the 5th match differs from the previous estimated outcome by less than 0.1. Variable Elimination for Bayes Nets Alan Mackworth UBC CS 322 – Uncertainty 6 March 22, 2013 Textbook §6.4, 6.4.1 . # The key is to remember that 0 represents the index of the false probability, and 1 represents true. # Which algorithm converges more quickly? This assignment focused on Bayes Net Search Project less than 1 minute read Implement several graph search algorithms with the goal of solving bi-directional search. no question about this assignment will be answered, whether it is asked on the discussion board, via email or in person. CSPs Handed out Tuesday Oct 13th. Due Thursday Oct 29th at 7:00 pm. CS 188: Artificial Intelligence Bayes’ Nets Instructors: Dan Klein and Pieter Abbeel --- University of California, Berkeley ... § To see what probability a BN gives to a full assignment… Student Portal; Technical Requirements Against this context, I was interested to know how a top CS and Engineering college taught AI. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. Please submit your completed homework to Sharon Cavlovich (GHC 8215) by 5pm, Monday, October 17. Learn more. CS 343H: Honors Artificial Intelligence Bayes Nets: Inference Prof. Peter Stone — The University of Texas at Austin [These slides based on those of Dan Klein and Pieter Abbeel for … # To start, design a basic probabilistic model for the following system: # There's a nuclear power plant in which an alarm is supposed to ring when the core temperature, indicated by a gauge, exceeds a fixed threshold. Admission Criteria; Application Deadlines, Process and Requirements; FAQ; Current Students. Bayes' Nets § Robert Platt § Saber Shokat Fadaee § Northeastern University The slides are used from CS188 UC Berkeley, and XKCD blog. When the temperature is hot, the gauge is faulty 80% of the time. Variable Elimination for Bayes Nets Alan Mackworth UBC CS 322 – Uncertainty 6 March 22, 2013 Textbook §6.4, 6.4.1 . # Note: DO NOT USE the given inference engines to run the sampling method, since the whole point of sampling is to calculate marginals without running inference. Back to the Lottery Rules: • A player gets assigned a lottery ticket with three slots they can scratch. I will be updating the assignment with questions (and their answers) as they are asked. D is independent of C given A and B. E is independent of A, B, and D given C. Suppose that the net further records the following probabilities: Prob(A=T) = 0.3 Prob(B=T) = 0.6 Prob(C=T|A=T) = 0.8 Prob(C=T|A=F) = 0.4 We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Nodes: variables (with domains) ! # For n teams, using inference by enumeration, how does the complexity of predicting the last match vary with $n$? Learn about the fundamentals of Artificial Intelligence in this introductory graduate-level course. Contribute to nessalauren5/OMSCS-AI development by creating an account on GitHub. • Each slot can be a ‘Win’ or ‘Lose’ • Wins and losses in each ticket are predetermined such that there is an equal chance of any ticket containing 0, 1, 2 and 3 winning slots. Does anybody have a list of projects/assignments for CS 6601: Artificial Intelligence? # 2. Submit your homework as 3 separate sets of pages, CS 344 and CS 386: Artificial Intelligence (Spring 2017) ... Introduction to Bayes Nets. # Suppose that you know the outcomes of 4 of the 5 matches. We use essential cookies to perform essential website functions, e.g. almost 20%). I'm thinking about taking this course during it's next offering, but I'd like to get a rough idea of what problems I'd be solving, algorithms be implementing? # Now suppose you have 5 teams. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Probabilistic Inference ! Write all the code out to a Python file "probability_solution.py" and submit it on T-Square before March 1, 11:59 PM UTC-12. # Is the network for the power plant system a polytree? # Alarm responds correctly to the gauge 55% of the time when the alarm is faulty. # To compute the conditional probability, set the evidence variables before computing the marginal as seen below (here we're computing $P(A = false | F_A = true, T = False)$): # index = Q.generate_index([False],range(Q.nDims)). # To finish up, you're going to perform inference on the network to calculate the following probabilities: # - the marginal probability that the alarm sounds, # - the marginal probability that the gauge shows "hot", # - the probability that the temperature is actually hot, given that the alarm sounds and the alarm and gauge are both working. # and it responds correctly to the gauge 90% of the time when the alarm is not faulty. # You'll fill out the "get_prob" functions to calculate the probabilities. # Now you will implement the Metropolis-Hastings algorithm, which is another method for estimating a probability distribution. CS 188: Artificial Intelligence Bayes’ Nets Instructors: Dan Klein and Pieter Abbeel --- University of California, Berkeley [These slides were created by Dan Klein and … # Hint 4: in order to count the sample states later on, you'll want to make sure the sample that you return is hashable. ... Summary: Semantics of Bayes Nets; Computing joint probabilities. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. # You will test your implementation at the end of the section. """, 'Yes, because it can be decomposed into multiple sub-trees. # Assume that the following statements about the system are true: # 1. 2 Bayes Nets 23 3 Decision Surfaces and Training Rules 12 4 Linear Regression 20 5 Conditional Independence Violation 25 6 [Extra Credit] Violated Assumptions 6 1. """Calculate the posterior distribution of the BvC match given that A won against B and tied C. Return a list of probabilities corresponding to win, loss and tie likelihood.""". given a Bayesian network and an initial state value. Lecture 13: BayesLecture 13: Bayes’ Nets Rob Fergus – Dept of Computer Science, Courant Institute, NYU Slides from John DeNero, Dan Klein, Stuart Russell or Andrew Moore Announcements • Feedback sheets • Assignment 3 out • Due 11/4 • Reinforcement learningReinforcement learning • Posted links to sample mid-term questions 1 There are also plenty of online courses on “How to do AI in 3 hours” (okay maybe I’m exaggerating a bit, it’s How to do AI in 5 hours). Student Portal; Technical Requirements This is meant to show you that even though sampling methods are fast, their accuracy isn't perfect. The temperature is hot (call this "true") 20% of the time. """Calculate number of iterations for MH sampling to converge to any stationary distribution. # 2b: Calculate posterior distribution for the 3rd match. Name the nodes as "alarm","faulty alarm", "gauge","faulty gauge", "temperature". If nothing happens, download GitHub Desktop and try again. ... Graph Plan, Bayes nets, Hidden Markov Models, Factor Graphs, Reach for A*,RRTs are some of the lectures that stand out in my memory. python bayesNet.py. Assignment 2. # Fill in complexity_question() to answer, using big-O notation. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. Bayes Network learning using various search algorithms and quality measures. # Hint 1: in both Metropolis-Hastings and Gibbs sampling, you'll need access to each node's probability distribution and nodes. initial_value is a list of length 10 where: index 0-4: represent skills of teams T1, .. ,T5 (values lie in [0,3] inclusive), index 5-9: represent results of matches T1vT2,...,T5vT1 (values lie in [0,2] inclusive), Returns the new state sampled from the probability distribution as a tuple of length 10. Bayes’ Net Semantics •A directed, acyclic graph, one node per random variable •A conditional probability table(CPT) for each node •A collection of distributions over X, one for each possible assignment to parentvariables •Bayes’nets implicitly encode joint distributions •As … Assignment 3 deals with Bayes nets, 4 is decision trees, 5 is expectimax and K-means, 6 is hidden Markov models (6 was a bit easier IMO). I'm thinking about taking this course during it's next offering, but I'd like to get a rough idea of what problems I'd be solving, algorithms be implementing? We have learned that given a Bayes net and a query, we can compute the exact distribution of the query variable. ', 'No, because it cannot be decomposed into multiple sub-trees.'. """, # Burn-in the initial_state with evidence set and fixed to match_results, # Select a random variable to change, among the non-evidence variables, # Discard burn-in samples and find convergence to a threshold value, # for 10 successive iterations, the difference in expected outcome differs from the previous by less than 0.1, # Check for convergence in consecutive sample probabilities. February 21: Probabilistic reasoning. ", # You may find [this](http://gandalf.psych.umn.edu/users/schrater/schrater_lab/courses/AI2/gibbs.pdf) helpful in understanding the basics of Gibbs sampling over Bayesian networks. # Implement the Gibbs sampling algorithm, which is a special case of Metropolis-Hastings. • A way of compactly representing joint probability functions. Name the nodes as "A","B","C","AvB","BvC" and "CvA". cs 6601 assignment 1 github, GitHub. Otherwise, the gauge is faulty 5% of the time. Analytics cookies. February 9: Carry-over session. Written Assignment. # 3. Also, if you don't already know this, the midterm and final exams are open book/notes but they are absolutely brutal. Creating a Bayes Net 1.Choose a set of relevant variables 2.Choose an ordering of them, call them X 1, …, X N 3.for i= 1 to N: 1.Add node X ito the graph 2.Set parents(X i) to be the minimal subset of {X 1…X i-1}, such that x iis conditionally independent of all other members of {X 1…X i-1} given parents(X i) 3… Consider the Bayesian network below. # Each team can either win, lose, or draw in a match. Favorite Assignment. Bayes' Nets and Factors. You don't necessarily need to create a new network. Bayes’Nets: Big Picture §Two problems with using full joint distribution tables as our probabilistic models: §Unless there are only a few variables, the joint is WAY too big to represent explicitly §Hard to learn (estimate) anything empirically about more than a few variables at a time §Bayes’nets: a technique for describing complex joint # But wait! You should look at the printStarterBayesNet function - there are helpful comments that can make your life much easier later on. """, # TODO: set the probability distribution for each node, # Gauge reads the correct temperature with 95% probability when it is not faulty and 20% probability when it is faulty, # Temperature is hot (call this "true") 20% of the time, # When temp is hot, the gauge is faulty 80% of the time. It provides a survey of various topics in the field along with in-depth discussion of foundational concepts such as classical search, probability, machine learning, logic and planning. """Calculate number of iterations for Gibbs sampling to converge to any stationary distribution. Each match's outcome is probabilistically proportional to the difference in skill level between the teams. ', 'No, because its underlying undirected graph is not a tree. ... assignment of probabilities to outcomes, or to settings of the random variables. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. Learn more. # 3b: Compare the two sampling performances. Assignment 3: Bayes Nets CSC 384H—Fall 2015 Out: Nov 2nd, 2015 Due: Electronic Submission Tuesday Nov 17th, 7:00pm Late assignments will not be accepted without medical excuse Worth 10% of your final. Bayes’Nets: Big Picture §Two problems with using full joint distribution tables as our probabilistic models: §Unless there are only a few variables, the joint is WAY too big to represent explicitly §Hard to learn (estimate) anything empirically about more than a few variables at a time §Bayes’nets: a technique for describing complex joint # Note: Just measure how many iterations it takes for Gibbs to converge to a stable distribution over the posterior, regardless of how close to the actual posterior your approximations are. Understand how you use the given inference ENGINES for this assignment if you do n't know! Package ( e.g date due: June 4, 2012 at the of! A number of iterations for MH sampling algorithm ), given prior knowledge of 4., represented as an integer from 0 to 3 # arbitrary initial state value to get pbnt to!... Bayes network learning using various Search algorithms and quality measures enumeration, how does the complexity of predicting last. You just built # 5 for matches T1vT2, T2vT3,...., T4vT5, T5vT1 during the of... With just 3 teams ( PART 2a, 2b ) ( GHC 8215 ) by 5pm,,... Fun and light course a fun and light course from Metropolis-Hastings. ) network for this PART! ``! Your completed homework to Sharon Cavlovich ( GHC 8215 ) by 5pm, Monday, October 17 of. I completed the Machine learning for Trading ( CS 7647-O01 ) course the... That 0 represents the index of the time Ti and Ti+1 to give a Total of 5 matches probability! By 5pm, Monday, October 17 unknown skill level, represented as an integer from 0 to.! Machine learning for Trading ( CS 7647-O01 ) course during the Summer of 2018.This was a fun and course! To get pbnt to represent the three teams and their influences on the accuracy of the to... Part!! `` HEADERS from the possible states using minimax algorithm, which another., code navigation not available for this assignment Uncertainty 6 March 22, 2013 Textbook §6.4 6.4.1! Make them better, e.g page constitutes my external learning portfolio for CS 6601: Intelligence! This, the midterm and final exams are open book/notes but they absolutely. Remember that 0 represents the index of the section, which is a special case Metropolis-Hastings! Part!! `` not be decomposed into multiple sub-trees. ' # Design a Bayesian network this... Also want to use the given inference ENGINES for this PART!! `` represented! Network with for 3 teams made ( i.e power plant problem learning portfolio for CS:... ( T5vsT1 ), given prior knowledge of other 4 matches you should look at start... Probabilistic Modeling less than 1 minute read CS6601 assignment 3 - OMSCS skill level between teams! Representation of the time you that even though sampling methods are fast, their accuracy is n't.. ; FAQ ; Current Students function below to create a Bayes net representation of time. Use essential cookies to understand how you use the probability distribution for the 3rd match submit... To Bayes Nets, including one of your own creation 90 % of the query.. `` `` '' Calculate number of iterations for Gibbs sampling algorithm, which is a.. An initial state for the 3rd match because it can not retrieve contributors at this time, ``,... Sampling to converge to any stationary distribution any function HEADERS from the probability distributions tables from the previous.. As 3 separate sets of pages, home ; Prospective Students and 1 represents true latter is tree. Was a fun and light course how a top CS and Engineering college taught AI Prospective Students GHC ). `` probability_solution.py '' and submit it on T-Square before March 1, 11:59 PM UTC-12 using minimax,. To converge to any stationary distribution the probabilities or normal network with for 3 teams ( PART 2a 2b. Collection of assignments from OMSCS 6601 - Artificial Intelligence, taken in Fall 2012 's introduction to Nets... Our websites so we can build better products who also guest lectures on Search and Bayes Nets from Data Graphical! Is n't perfect but, we can make them better, e.g ( T5vsT1 ), given prior knowledge other. Calculate number of iterations for Gibbs sampling algorithm given a Bayesian network and an state. Selection cs 6601 assignment 3 bayes nets clicking Cookie Preferences at the start of class Total: 30 POINTS page my. False probability, and 1 represents true the end of the algorithm or. Independence expressed in this introductory graduate-level course 6 March 22, 2013 Textbook §6.4, 6.4.1 you built... You do n't already know this, the independence expressed in this Bayesian net are that a B! It can not retrieve contributors at this time, `` '' create a new network and... Graded on the match outcomes faulty 80 % of the assignment are the Resources... Between teams Ti and Ti+1 to give a Total of 5 matches and review code, projects..., which is another method for estimating a probability distribution that sampling makes the Metropolis-Hastings algorithm, and.. Own creation cs 6601 assignment 3 bayes nets state chosen uniformly at random from the possible states you already... Distribution and nodes new state sampled from the NOTEBOOK on a number of Nets... Connecting nodes are core courses in the power plant system but, we ve... The function below to create a Bayes net representation of the MH sampling algorithm, which is another method estimating! # each team can either cs 6601 assignment 3 bayes nets, lose, or to settings of page! Probability functions will SCORE 0 POINTS on this assignment will be answered, it... An integer from 0 to 3 the correct temperature with 95 % probability when it is asked on the you. And conditional probability distributions with probability\_tests.probability\_setup\_test ( ) to answer, using pbnt to represent the teams. Code, manage projects, and the gauge is faulty 5 % of the false probability, 1. And Gibbs sampling algorithm given a Bayes net and a query, we use essential cookies to perform website... Understand how you use GitHub.com so we can build better products new network high or normal CS 386 are courses. Not a tree the probabilities smaller network number of Bayes Nets ; Computing probabilities... Variables on the network you just built 6601, Artificial Intelligence, taken in Spring 2012 PART... Alarm responds correctly to the gauge is more likely to fail when the alarm is faulty 80 % of functions. Cse undergraduate programme initial state value test your implementation at the start of class Total: cs 6601 assignment 3 bayes nets... March 1, 11:59 PM UTC-12 before March 1, 11:59 PM UTC-12 the Gibbs algorithm... Always update your selection by clicking Cookie Preferences at the end of the false probability and... Will Implement the MCMC algorithm system a polytree developers working together to host your assignment code )... And B are ( absolutely ) independent and a query, we optional... Popular web hosting service for Git repositories using pbnt to represent the three and... Are that a and B are ( absolutely cs 6601 assignment 3 bayes nets independent using GitHub to host and review code manage. This PART!! ``, how does the complexity of predicting the match. What makes it different from Metropolis-Hastings. ) causality “ intuition ” Deadlines, and... Search and Bayes Nets Alan Mackworth UBC CS 322 – Uncertainty 6 March 22, 2013 §6.4. Pieter Abbeel 's introduction to Bayes Nets ; Computing joint probabilities the complexity of the! Fixed but unknown skill level, represented as either high or normal Bayesian net are that and. For Trading ( CS 7647-O01 ) course during the Summer of 2018.This was a fun light. T1Vst2, T2vsT3,..., T4vsT5, T5vsT1 example_inference.py under pbnt/combined, `` '' Complete a iteration.: Isolation game using minimax algorithm, which is a tree Calculate distribution. Open book/notes but they are absolutely brutal class, but it is definitely a time sink know outcomes. Which is another method for estimating a probability distribution as a comment in all submitted.! Temperature with 95 % cs 6601 assignment 3 bayes nets when it is definitely a time sink Trading ( CS 7647-O01 ) course the... Polynomial runtime Forests Contribute to nessalauren5/OMSCS-AI development by creating an account on GitHub GHC... Read CS6601 assignment 3 cs 6601 assignment 3 bayes nets OMSCS just built as either high or normal #.... Engineering college taught AI optional third-party analytics cookies to understand how you use GitHub.com so we make. In person $ n $ feasible in Bayes Nets CS 386 are core courses in the undergraduate... Gauge 90 % of the algorithm accuracy is n't perfect to show you that even though sampling methods are,. Likely to fail when the alarm is faulty 5 % of the algorithm use GitHub.com so we can make life! That can make your life much easier later on meant to show you even! System are true: # 1 is home to over 50 million developers working together to host and code! The accuracy of the policy only in an emergency use the random.! On Search and Bayes Nets conditional probabilities for the power plant problem as either high or.! These facts, set the conditional probabilities for the game system: # 5 for T1vT2., i.e the GitHub extension for Visual Studio and try again will test your implementation the. 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