Bayesian Reasoning and Machine Learning
David Barber c 2007,2008,2009,2010,2011,2012,2013,2014,2015,2016
Notation List
V
a calligraphic symbol typically denotes a set of random variables . . . . . . . . 7
dom(x)
Domain of a variable . . . . . . . . . . .
Dirichlet Process
Yee Whye Teh, University College London
Related keywords: Bayesian nonparametrics, stochastic processes, clustering,
infinite mixture model, Blackwell-MacQueen urn scheme, Chinese restaurant
process.
Definition
The Dirichlet process is a
A Tutorial on Particle Filtering and Smoothing:
Fifteen years later
Arnaud Doucet
The Institute of Statistical Mathematics,
4-6-7 Minami-Azabu, Minato-ku,
Tokyo 106-8569, Japan.
Email: [email protected]
Adam M. Johansen
Department of Statistics,
University
Covariance Selection
Author(s): A. P. Dempster
Source: Biometrics, Vol. 28, No. 1, Special Multivariate Issue (Mar., 1972), pp. 157-175
Published by: International Biometric Society
Stable URL: http:/www.jstor.org/stable/2528966
Accessed: 07-07-2016 12:05
On Tight Approximate Inference of the Logistic-Normal Topic
Admixture Model
Amr Ahmed
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
[email protected]
Abstract
The Logistic-Normal Topic Admixture Model
(LoNTAM), also known as
A Spectral Algorithm for Learning Hidden Markov Models
Daniel Hsu1,2 , Sham M. Kakade2 , and Tong Zhang1
1
arXiv:0811.4413v6 [cs.LG] 6 Jul 2012
2
Rutgers University, Piscataway, NJ 08854
University of Pennsylvania, Philadelphia, PA 19104
Abstract
Hidden M
A Spectral Algorithm for Latent Tree Graphical Models
Ankur P. Parikh
Le Song
Eric P. Xing
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Abstract
Latent variable models are powerful tools for
probabilistic modeling, and
Kernel Belief Propagation
1
Le Song,1 Arthur Gretton,1,2 Danny Bickson,1 Yucheng Low,1 Carlos Guestrin1
School of Computer Science, CMU; 2 Gatsby Computational Neuroscience Unit & MPI for Biological Cybernetics
Abstract
urally specified by continuous, non
Discriminative Random Fields
Sanjiv Kumar ([email protected])
Google Research, 1440 Broadway, New York, NY 10018, USA
Martial Hebert ([email protected])
The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Abstract.
In this resea
Conditional Random Fields: Probabilistic Models
for Segmenting and Labeling Sequence Data
John Laffertyy
LAFFERTY @ CS . CMU . EDU
Andrew McCallumy
MCCALLUM @ WHIZBANG . COM
FPEREIRA @ WHIZBANG . COM
Fernando Pereiraz
WhizBang! LabsResearch, 4616 Henry S
Gaussian Processes in Machine Learning
Carl Edward Rasmussen
Max Planck Institute for Biological Cybernetics, 72076 T
ubingen, Germany
[email protected]
WWW home page: http:/www.tuebingen.mpg.de/carl
Abstract. We give a basic introduction to Gaussian
Additional Exercises for Convex Optimization
Stephen Boyd
Lieven Vandenberghe
August 26, 2016
This is a collection of additional exercises, meant to supplement those found in the book Convex
Optimization, by Stephen Boyd and Lieven Vandenberghe. These exe
ERRATA for All of Statistics
1. p. 11. Second equation should be = not .
2. p. 13. Second P(B|A1 ) should be P(B|A3 ).
3. p. 26. line -10. These two binomials are assumed to be independent.
4. p. 26. bottom line. k is an integer.
5. p. 27. line 12. The Po
ERRATA for All of Statistics Second Printing
1. p. 27 line 2. Secomd sum. exponent should be k 1 not k.
2. p. 30 and p. 433. density for t-distribution missing sqrtnu pi in
denominator.
3. p. 58 line 2 of Example 3.35. Y = Y1 + Y2 .
4. p. 73 Example 5.3 l
Group Sparse Additive Models
Junming Yin
[email protected]
Xi Chen
[email protected]
Eric P. Xing
epx[email protected]
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Abstract
We consider the problem of sparse variable
sel
Kernel Embeddings of Latent Tree Graphical Models
Le Song
College of Computing
Georgia Institute of Technology
[email protected]
Ankur P. Parikh
School of Computer Science
Carnegie Mellon University
[email protected]
Eric P. Xing
School of Computer Sc
Decomposing a Scene into Geometric and Semantically Consistent Regions
Stephen Gould
Dept. of Electrical Engineering
Stanford University
Richard Fulton
Dept. of Computer Science
Stanford University
Daphne Koller
Dept. of Computer Science
Stanford Universi
Bayesian Haplotype Inference via the Dirichlet Process
Eric Xing
[email protected]
Roded Sharan
[email protected]
Michael I. Jordan]
[email protected]
]
Computer Science Division and Department of Statistics , University of California, Ber
Regularized Bayesian Inference and Infinite Latent SVMs
Bayesian Inference with Posterior Regularization
and applications to Infinite Latent SVMs
Jun Zhu
Ning Chen
[email protected][email protected]
State Key Laboratory of Intelligen
Learning via Hilbert Space Embedding of Distributions
by
Le Song
A thesis submitted to
The School of Information Technologies
The University of Sydney
for the degree of
DOCTOR OF PHILOSOPHY
June 1, 2008
c 2007
Le Song
All Rights Reserved
I hereby certify
Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees
Joseph E. Gonzalez
Carnegie Mellon University
[email protected]
Yucheng Low
Carnegie Mellon University
[email protected]
Abstract
Carlos Guestrin
Carnegie Mellon University
[email protected]
Partially Observed Maximum Entropy
Discrimination Markov Networks
Jun Zhu
Eric P. Xing
Bo Zhang
State Key Lab of Intelligent Tech & Sys, Tsinghua National TNList Lab, Dept. Comp Sci & Tech,
Tsinghua University, Beijing China. [email protected]; dcs
AN INTRODUCTION TO GRAPHICAL
MODELS
Michael I. Jordan
Center for Biological and Computational Learning
Massachusetts Institute of Technology
http:/www.ai.mit.edu/projects/jordan.html
Acknowledgments:
Zoubin Ghahramani, Tommi Jaakkola, Marina Meila
Lawrenc
Statistica Sinica 4(1994), 639—650
A CONSTRUCTIVE DEFINITION OF DIRICHLET PRIORS
J ayaram Sethuraman
Florida State University
Abstract: In this paper we give a simple and new constructive deﬁnition of Dirichlet
measures removing the restriction that the b
Model Choice using Reversible Jump Markov Chain
Monte Carlo,
David I. Hastie
Imperial College London.
Peter J. Green
University of Bristol.
May 11, 2011
Abstract
We review the across-model simulation approach to computation for Bayesian model determinatio
Colloquium
Finding scientific topics
Thomas L. Griffiths* and Mark Steyvers
*Department of Psychology, Stanford University, Stanford, CA 94305; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology,
Cambridge, MA 02139-4307; an
An Introduction to the Kalman Filter
Greg Welch1 and Gary Bishop2
TR 95-041
Department of Computer Science
University of North Carolina at Chapel Hill
Chapel Hill, NC 27599-3175
Updated: Monday, July 24, 2006
Abstract
In 1960, R.E. Kalman published his fa