CS228 Programming Assignment#1
1
CS 228, Winter 20092010 Programming Assignment #1Inference in Graphical Models
In this assignment you will implement the Sum Product Message Passing algorithm for exact inference in Graphical Models, and then extend the a
Probabilistic Graphical Models: Principles and Techniques
CS 228

Winter 2009
CS228 Problem Set #2 Solutions
1
CS 228, Winter 2007 Problem Set #2 Solutions
Handout #12
1. Collapsed Gibbs Sampling In this problem we will explore a few variations of Collapsed Gibbs sampling, as described in Section 10.5.2 (which you should read caref
Probabilistic Graphical Models: Principles and Techniques
CS 228

Winter 2009
Queries Outside a Clique
February 8, 2008
Consider a query P (Y  e) where the variables Y are not present together in a single clique. One naive approach is to construct a clique tree where we force one of the cliques to contain Y . However, this approac
Probabilistic Graphical Models: Principles and Techniques
CS 228

Winter 2009
CS228 Final
1
CS 228, Winter 2007 Final
Handout #17
You have 24 hours to complete this exam. The exam is given out at noon, and due at noon (12:00 pm) one day after you pick it up. The exam will be handed out and collected in Gates 120 (the Fishbowl). Thi
Probabilistic Graphical Models: Principles and Techniques
CS 228

Winter 2009
CS228 Final
1
CS 228, Winter 2008 Final
You have 24 hours to complete this exam. You must return the completed exam to Gates 120 (the Fishbowl) at either 12:00 pm or 6:00 pm the day after you receive the exam, depending on what time you chose to receive i
Probabilistic Graphical Models: Principles and Techniques
CS 228

Winter 2009
CS228 Problem Set #4
1
CS 228, Winter 2008 Problem Set #4
1. Parameter Estimation in TemplateBased Models [20 points] In class, we talked about parameter learning in the case of partially observed data for general Bayesian networks. Here, we apply these
Probabilistic Graphical Models: Principles and Techniques
CS 228

Winter 2009
Practice Questions for Quiz 4
CS228 Winter 2009 January 31, 2009
Note: This practice question was updated on Jan 31, 2009. The following questions are provided as examples of the types of questions we will ask on the quiz this week, as well as the kinds o
Probabilistic Graphical Models: Principles and Techniques
CS 228

Winter 2009
CS228 Problem Set #2
1
CS 228, Winter 2009 Problem Set #2 Solutions
For each problem, a number of error codes describing common mistakes made by students are listed below. If you feel that your homework has been wrongly graded, please come see us. All err
Probabilistic Graphical Models: Principles and Techniques
CS 228

Winter 2011
CS228 Programming Assignment #2
Stanford CS 228, Winter 20112012
1
Programming Assignment 2: Appendix
1 Overview of Genetics
In humans, the DNA is arranged in twentythree pairs of chromosomes. A person inherits one chromosome in each pair from his/her m
Probabilistic Graphical Models: Principles and Techniques
CS 228

Winter 2009
CS228 Final
1
CS 228, Winter 2006 Final
Handout #16
1. [8 points] Inuence Diagrams Consider a Inuence Diagram containing a single utility factor in which all variables other than D(a decision node) and its parents have been eliminated using standard VE. S
Probabilistic Graphical Models: Principles and Techniques
CS 228

Winter 2011
CS228 Problem Set #2
1
CS 228, Winter 20112012 Problem Set #2
This assignment is due at 12 noon on Feburary 6. Submissions should be placed in the filing cabinet labeled CS228 Homework Submission Box located in the lobby outside Gates 187. Suppose we wis
Probabilistic Graphical Models: Principles and Techniques
CS 228

Winter 2009
CS228 Problem Set #3
1
CS 228, Winter 2008 Problem Set #3
We have provided approximate lengths with each of the problems to give you a rough estimate of how long we think each answer might be not including diagrams. These are meant to be guidelines only t
Probabilistic Graphical Models: Principles and Techniques
CS 228

Winter 2009
CS228 Programming Assignment#1
1
CS 228, Winter 2009 Programming Assignment #1Inference in Graphical Models
In this assignment you will implement the Sum Product Message Passing algorithm for exact inference in Graphical Models, and then extend the algori
Probabilistic Graphical Models: Principles and Techniques
CS 228

Winter 2009
CS228 Problem Set #0 Solutions
1
CS 228, Winter 2008 Solutions to Problem Set #0: Probability Review
1. After your yearly checkup, the doctor has bad news and good news. The bad news is that you tested positive for a serious disease, and that the test is
Homework 1
CS228: Probabilistic Graphical Models
Instructor: Stefano Ermon
[email protected]
Available: Jan. 8 2015
Due date: January 20 2015
Problem 1: Probability theory (6 points)
The doctor has bad news and good news for X. The bad news is that X tes
Probabilistic Graphical Models: Principles and Techniques
CS 228

Winter 2011
CS228 Programming Assignment #3
1
Stanford CS 228, Winter 20112012 Programming Assignment #3: Markov Networks for OCR
This assignment is due at 12 noon on January 30.
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1.1
Overview
Introduction
In the last assignment, you used Bayesian networks to model
Probabilistic Graphical Models: Principles and Techniques
CS 228

Winter 2011
CS228 Programming Assignment #2
1
Stanford CS 228, Winter 20112012 Assignment #2: Bayes Nets for Genetic Inheritance
This assignment is due at 12 noon on January 23.
1
Overview
Because of your success in modeling creditworthiness, your fame as an expert
Probabilistic Graphical Models: Principles and Techniques
CS 228

Winter 2011
CS228 Programming Assignment #1
1
Stanford CS 228, Winter 20112012 Assignment #1: Introduction to Bayesian Networks
This assignment is due at 12 noon on January 16.
1
Overview
Welcome to CS228! The goal of this rst assignment is for you to gain familiari
CS228 Final Exam
1
CS 228, Winter 2016
Final Exam
This exam is worth 100 points. You have 3 hours to complete it. Good luck!
Stanford University Honor Code
The Honor Code is the Universitys statement on academic integrity written by students in 1921. It a
Quiz 1  CS 228, 2017
1. Which of the following apply to 2 independent events A, B (select 0  4 of them)?
a. P(A)P(B) = P(A, B)
b. P(A) + P(B) = P(A, B)
c. P(BA) = P(B)
d. P(A) + P(B) = 1
2. Mutual independence (P(X_1, , X_n) = P(X_1).P(X_n) pairwise in
Quiz 1  CS 228, 2017  solutions
1. Which of the following apply to 2 independent events A, B (select 0  4 of them)?
a. P(A)P(B) = P(A, B)
b. P(A) + P(B) = P(A, B)
c. P(BA) = P(B)
d. P(A) + P(B) = 1
Answers: a, c
2. Mutual independence (P(X_1, , X_n) =
Probabilistic Graphical Models
Stefano Ermon
Stanford University
Lecture 7, January 26, 2016
Stefano Ermon (AI Lab)
Graphical Models
Lecture 7, January 26, 2016
1 / 35
Todays lecture
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How to solve multiple probabilistic inference queries on the same
grap
This is Example 9.1 from the book.
What is p(Job)? Joint distribution factorizes as:
p(C, D, I, G, S, L, H, J) = p(C)p(DC)p(I)p(GD, I)p(LG)P (SI)P (JS, L)p(HJ, G)
with factors
= cfw_C (C), D (C, D), I (I), G (G, D, I), L (L, G), S (S, I), J (J, S,
Probabilistic Graphical Models
Stefano Ermon
Stanford University
Lecture 16, March 2, 2017
Stefano Ermon (AI Lab)
Graphical Models
Lecture 16, March 2, 2017
1 / 36
Today: structure learning
Goals for the lecture. You should understand the following concep
CS228 Homework 4
Instructor: Stefano Ermon [email protected]
Available: 02/17/2017; Due: 03/3/2017
1. [20 points] We have a data association problem where there are K objects and we are given K observations. Each observation corresponds to a single objec
Markov chain Monte Carlo
Lecture 10
Stefano Ermon
Stanford
Slides adapted from Eric Xing, Qirong Ho (CMU), and David
Sontag (NYU)
1
Announcements
Homework 3 is out. Start EARLY.
Online midquarter feedback opens today.
2
Limitations of Monte Carlo
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