The following resources are useful for learning Bayesian analysis.

Books

• Probabilistic Programming & Bayesian Methods for Hackers, by camdp
• Doing Bayesian Data Analysis, 2nd ed, by J. Kruschke
• Bayesian Data Analysis, by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin (1995, 2003, 2013)
• Handbook of Markov Chain Monte Carlo, edited by Brooks, Gelman, Jones, and Meng (2011)
• Data Analysis Using Regression and Multilevel/Hierarchical Models, by Gelman and Hill (2007)
• Applied Bayesian Modelling, 2nd ed, by Congdon.

Software

• pyMC

  ==> github https://github.com/aloctavodia/Doing_bayesian_data_analysis
  ==> BEST:  https://github.com/strawlab/best
  ==> The time has come: Bayesian methods for data analysis in the organizational sciences
  

• emcee
• Infer.NET

  ==> Microsoft TrueSkill
  

Learning

• Chris Fonnesbeck: SciPy 2014 Tutorial, Video 1,2,3

  ==> github https://github.com/fonnesbeck/scipy2014_tutorial
  

• Business 41000
• Frequentism and Bayesianism: A Practical Introduction 4 part series
• Bayesian Estimation Supersedes the t-test (BEST) - online
• MLSS Iceland 2014 videos

Research

• Nicholas Polson's research papers on finance/economics.
• automatic graphical model construction (structural inference over dynamic Bayesian networks)

Blogs:

* http://doingbayesiandataanalysis.blogspot.com
* https://jakevdp.github.io/
* https://twiecki.github.io/
* http://camdp.com/blogs/
* http://probablisticprogramming.net
* http://healthyalgorithms.com/

Machine Learning

* http://hunch.net
* http://fastML.com