Slides for NY Open Statistical Programming Meetup

Introductory Survey of Bayesian Methods Considering Dynamic Linear Models(pdf), a talk given to the NY Open Statistical Programming Meetup on 16 March 2016.


A primer in Bayesian Inference by Aart F. de Vos

Introduction to Bayesian Inference

Jaynes's "Probability Theory: The Logic of Science"

Unofficial Errata and Commentary [for the above]

Video lecture by Sharon McGrayne on her book "The Theory That Would Not Die"

What is Bayesian statistics and why everything else is wrong

General Bayesian

Bayesian Inference Resources

Bayesian Thinking blog

Conditional Probability: a visual explanation

Motivating the Bayesian prior with de Finetti's theorem

de Finetti was right: Probability does not exist

Bayesian Software

Bayesian Inference on A Binomial Proportion (R)

The BUGS Project Graphical model software links


JAGS with R

Matlab: various packages

Microsoft's component-based Windows application for creating, assessing, and evaluating Bayesian Networks

Open BUGS (Bayesian Inference Using Gibbs Sampling)

R2OpenBUGS: A Package for Running OpenBUGS from R

R dlm package

R for Bayesian statistics

R packages used for Bayesian inference

Think Bayes (Python)

"Think Bayes" author

Wolfram interactive CDF player

Bayesian Inference on a Binomial Proportion

Bayesian Networks

A Tutorial on Learning With Bayesian Networks by David Heckerman, March 1995 (tr-95-06.pdf)

A Brief Introduction to Graphical Models and Bayesian Networks, by Kevin Murphy, 1998

Working Examples of Bayesian Networks

Boosted Learning in Dynamic Bayesian Networks for Multimodal Speaker Detection by Garg, Pavlovic, Rehg

Recognizing multi-modal sensor signals using evolutionary learning of dynamic Bayesian networks by Young-Seol Lee • Sung-Bae Cho

Multi-Sensor Fusion Method using Dynamic Bayesian Network for Precise Vehicle Localization and Road Matching by Cherif Smaili, Maan E. El Najjar, François Charpillet

Dynamic Linear Models

Bayesian Financial Dynamic Linear Model

Dynamic Linear Models with R by Petris, et al.

Good working paper by Mike West

Introduction to Dynamic Linear Models

Time series and dynamic linear models

Gibbs Samplers, other MCMC-based Techniques

Hidden Markov Models in R


Markov Chain Monte Carlo and Applied Bayesian Statistics

Self-contained intro to the Metropolis-Hastings algorithm

Learning With Hidden Variables

See "Open BUGS" above