Relevant Links for My Research

Graphical Models and Inference Algorithms

• Murphy's Introduction to Graphical Models and Bayesian Networks.
• "Inference in Belief Networks: A procedural guide". C. Huang and A. Darwiche, 1996.
• "A Tutorial in Hidden Markov Models and Selected Applications in Speech Recognition". L. R. Rabiner, 1989.
• "Graphical models, exponential families, and variational inference". M. J. Wainwright and M. I. Jordan, 2003.
• "Tutorial on Variational Approximation Methods". Tommi S. Jaakkola, 2000.

Sampling Agorithm and Object Tracking

• "An introduction to Monte Carlo methods". D. MacKay, 1998.
• "A Tutorial on Particle Filters for On-line Non-linear/Non-Gaussian Bayesian Tracking". S. Arulampalam, et al. 2001.
• Monte Carlo strategies in Science Computing, by Jun Liu.
• Monte Carlo Statistical Methods.
• Fundamentals of Kalman filtering : a practical approach, by Paul Zarchan and H. Musoff.

Mathematics

• All of statistics : a concise course in statistical inference. by L. Wasserman.
• Statistical inference, by George Casella, Roger L. Berger.
• Statistical Decision Theory and Bayesian Analysis, by James Berger.
• EM algorithm and extensions, by McLachlan.
• Tools for Statistical Inference, by Tanner.
• Introduction to linear algebra, by Mirsky.
• Linear Algebra, by Peter Lax.
• Introduction to applied mathematics, by Gilbert Strang.
• Contemporary abstract algebra, by Joseph A. Gallian.
• Functional analysis, by Peter D. Lax.
• Introductory functional analysis with applications, by Kreysizig.
• Differential equations, with applications and historical notes, by George F. Simmons.
• Riemannian Manifolds, by John Lee.
• Introduction to analysis, by Maxwell Rosenlicht.
• How to Solve It, by G. Polya
• Math Career Advice , by T. Tao
• Graduate School Research Advice , Stanford

Linux and Tech

• Understanding the Linux kernel .
• The Art of Project Management, by Scott Berkun .

Recreation

• Origin of Species, by Darwin .
• CS685, by Jon Kleinberg @ sp2006.