### Books on probability and statistics

Below we indicate some useful books. We suggest to see Amazon for further comments to decide which book best suites you.

**A simple introduction to probability and statistics for a broad audience, with gentle mathematical use**

Sanjeev Kulkarni, Gilbert Harman: "An Elementary Introduction to Statistical Learning Theory"

**Recommended by many, from Stanford**

Hastie, Tibshirani, Friedman: "The Elements of Statistical Learning"

**Nice book covering much of the statistics and probability used in machine learning books**

Larry Wassermann: "All of Statistics: A Concise Course in Statistical Inference"

**Another book suggested by others**

Dimitri P. Bertsekas, John N. Tsitsiklis "Introduction to Probability, 2nd Editionby"

WEB Resources on Statistics

**A Brief Introduction to Graphical Models and Bayesian Networks (By Kevin Murphy, 1998; web-page with tutorial and other resources)**

http://people.cs.ubc.ca/~murphyk/Bayes/bnintro.html

**Bayes Net Toolbox for Matlab (Written by Kevin Murphy, 1997--2002. Last updated: 19 October 2007)**

https://code.google.com/p/bnt/

### Online free course on statistics and probability

**Stanford's Probability and Statistics**

Course with no deadlines self-paced course, no statement of accomplishment, free of cost, no credit, no verification, world class material:

http://online.stanford.edu/course/probability-and-statistics-self-paced

**A coursera course focussed on R**

Course on statistic and probability, focussed on R, one of the most important software for statistical analysis become a standard in scintific research (it is also open-source):

Duke U., Statistics with R:

https://www.coursera.org/specializations/statistics

### Statistics applied to neuroscience

**Statistical Parametric Mapping (and Dynamic Causal Modelling; by University College London):**

http://www.fil.ion.ucl.ac.uk/spm/