ECE 175B Homework 1 Solutions Spring 2015
1. [BRML 1.1]
First part:
p(y, x|z) = p(x, y|z) =
p(x, y, z)
p(z)
p(y|x, z)p(x, z)
p(y|x, z)p(x|z)
=
=
= p(y|x, z)p(x|z) .
p(z)
p(z)
p(z)
An even simpler proof is to note that the product rule of conditional expec
ECE 175B Homework 2 Solutions V.2 Spring 2015
1. [BRML 3.1]
Many of these problems are quite tedious to compute by hand and they demonstrate that the solution
complexity can quickly get out of hand, even for simple graphs. This is the reason that students
ECE 174
Supplemental Solutions to Homework 2
The material presented below supplements the solutions which you can find in
the textbooks Solutions Manual (which all of you should have).
Meyer 4.1.1. Is the subset of Rn a vector subspace? Since Rn is a vect
Probability Theory
as an
Uncertainty Calculus
Ken Kreutz-Delgado
UC San Diego - ECE 175B Spring 2015
Version 1.0b
Why an Uncertainty Calculus?
Models are always approximations and simplifications of
complex situations due to inherent limitations in knowl
ECE 175B
Probabilistic Reasoning
& Graphical Models
[From Bishop 2006]
Ken Kreutz-Delgado
ECE Department - UC San Diego
Spring Quarter 2015
Contact Information
! Course Website
Accessible from http:/dsp.ucsd.edu/~kreutz
! Instructor
Ken Kreutz-Delgado
k
Making Sense of a Complex World
Ken Kreutz-Delgado
Professor of Intelligent Systems & Machine Learning
ECE Department JSOE UCSD
In the beginning the Earth was
without form (Genesis, KJV)
(Jackson Pollack)
The
Introduction to Graphical Models1
David Barber
University College London
1
These slides are modied from the slides accompany the book Bayesian Reasoning and Machine Learning. The original, unmodied, slides are located
at www.cs.ucl.ac.uk/staff/D.Barber/br
ECE 175B Homework 5 Solutions Spring 2015
1. [BRML 5.1] We are to determine an ecient algorithm for computing the partition function, Z, for
the pdf of a tree1 containing N nodes:
Z=
(xi , xj ) .
X ij
If one visualizes a rooted tree2 , then one can visual
PGM Programming Assignment #4
1
Probabilistic Graphical Models
Assignment #4: Exact Inference
This assignment is due at 11:59pm PDT (UDT -7) on 17 April 2012.
1
Overview
In Programming Assignment 1, you implemented a rudimentary inference engine that coul
ECE 175B Project 4: Spring 2015
Matthew D Burns
June 2, 2015
Due date: 9AM, 12 Jun 2015
Start by doing PGM Programming Assignment 4.pdf .
Expect to write more lines of code for this project than the last two.
This project will be hand graded, as it is
PGM Programming Assignment #3
1
Probabilistic Graphical Models
Assignment #3:
Markov Networks for OCR
This assignment is due at 11:59pm PDT (UDT -7) on 10 April 2012.
1
Overview
1.1
Introduction
In the last assignment, you used Bayesian networks to model
PGM Programming Assignment #1
1
Probabilistic Graphical Models
Assignment #1:
Introduction to Bayesian Networks
This assignment is due at 11:59PM PDT (UDT -7) on 27 March 2012.
1
Overview
Welcome to the course!
The goal of this rst assignment is for you t
SamIam version 3.0 2010-01-15
SamIam 3.0 features two new inference algorithms. Both are approximate algorithms, useful for large models for which exact inference is
impossible.
1. loopy belief propagation
2. automatic edge deletion belief propagation
by
ECE 175B Projects: Winter 2015
March 31, 2015
Contact
Matt Burns
[email protected]
Oce hour: TBD
Please use the following subject line for all emails:
[ECE 175B], Project xx - Your Name
Do not send emails for technical questions. Post them to Piazza.
O
Readme for SamIam Release 3.0
Instructions:
This readme describes the Mac OS X i386 release of SamIam version 3.0. The designation i386 means that we
compiled the supplementary libraries supplied with this version (libcallsmile.jnilib, libcalljvmti.jnilib
ECE 175B Project 1: Winter 2015
March 31, 2015
Due date: 21 Apr 2015
1. Download and Install SAMIAM: http:/reasoning.cs.ucla.edu/samiam/
index.php?s=
Windows users may have to change samiam.bat line:
call %EXECCMD% %VMARGS% -launchcommand %EXECCMD% %VMAR