Sincere thanks to Dr. Shantanu Desai For his extensive guidance and research

This repository is a "starters kit" of useful statistical tools needed for analysis of data. It will discuss usage of statistics in Astronomy and Particle Physics and what tools are need to tackle problems in the above fields. However, the same statistical techniques are used in all branches of Physics and Engineering. The assignments are written in python however they can be done in any programming language.

Statistics, Data Mining and Machine Learning in Astronomy by Z. Ivezic, AJ. Connolly, Jake Vanderplas and Alex Gray (see also the webpage for this book library at http://www.astroml.org) 2nd edition also available. Other useful books (as reference)

Numerical Recipes 2nd edition, by Press et al (de-facto reference for many years to astrophysicists and particle physicists especially in frequentist analysis) Python Data Science handbook by Jake Van Der Plas (very useful introduction to Python based data analysis. Came out in 2019)

Practical Statistics for Astronomers J.V. Wall and C.R. Jenkins (See http://www.astro.ubc.ca/people/jvw/ASTROSTATS/) (somewhat advanced and specialized)

Statistical Data Analysis by Glenn Cowan (for particle physicists)

Statistics for Nuclear and Particle Physicists by Louis Lyons

Statistics by R. J. Barlow

Data Analysis: A Bayesian Tutorial by D. Sivia and John Skilling (a must read for Bayesian aficionados. somewhat advanced for this course)

Modern Statistical Methods for Astronomy with R applications by Eric Feigelson and G.J. Babu (R based)

1. Frequentism and Bayesianism a Practical Intro

2. When Results differ

3. Confidence and Credibility

4. Bayesian in python

5. Model Selection

Samplers Samplers-everywhere !!!

• https://arxiv.org/abs/0712.3028 A practical guide to basic statistical techniques for data analysis in cosmology

• https://arxiv.org/abs/1008.4686 Data analysis recipes: fitting a model to data 50 page primer on fitting a straight line to data

• https://arxiv.org/abs/0803.4089 Bayesian inference and model selection in cosmology

• https://arxiv.org/abs/1012.3754 Dos and don'ts of reduced chi-square

• https://arxiv.org/abs/1701.01467 Bayesian methods in Cosmology (2017)

• https://arxiv.org/abs/1710.06068 Data Analysis Recipes : Using Markov Chain Monte Carlo

• https://arxiv.org/abs/1009.2755 Error estimation in astronomy: A guide

• https://arxiv.org/pdf/1901.07726.pdf Model Selection in Cosmology

• https://arxiv.org/abs/1809.02293 An introduction to Bayesian inference in gravitational-wave astronomy

• https://arxiv.org/abs/1909.12313 A conceptual introduction to Markov Chain Monte-Carlo methods

• https://arxiv.org/abs/1012.3589 Topics in statistical data analysis for high energy Physics

• https://arxiv.org/pdf/1307.2487v1.pdf Statistics for Searches at LHC

• https://arxiv.org/abs/1607.03549 Statistical Issues in Neutrino Physics Analysis

• https://arxiv.org/abs/1301.1273 Bayes and Frequentism: a particle physicist's perspective

• https://arxiv.org/abs/1503.07622 Statistics for LHC

• https://arxiv.org/abs/1807.05996 Lectures on Statistics in Theory: Prelude to Statistics in Practice

• https://arxiv.org/pdf/1905.12362.pdf Practical statistics for particle physics

• https://www-cdf.fnal.gov/physics/statistics/notes/H0H1.pdf Lecture notes on model comparison

• https://arxiv.org/pdf/1911.10237.pdf Searching for new physics with profile likelihoods : wilks and beyond

• https://arxiv.org/abs/2009.06864 Thomas Junk and Louis Lyons

https://jellis18.github.io/post/2018-01-02-mcmc-part1/

• https://www.pp.rhul.ac.uk/~cowan/stat_course.html

• http://physics.rutgers.edu/~gawiser/689/ (follows the same textbook as this)

• http://www.astro.ubc.ca/people/jvw/ASTROSTATS/lectures/list.html

• http://www.phy.duke.edu/~schol/phy771/ (check out FAQ)

• https://www.stat.tamu.edu/~jlong/astrostat/ (Fall 2016 SAMSI course on astrostatistics)

• http://hosting.astro.cornell.edu/~cordes/A6523/

• https://github.com/dirac-institute/uw-astr598-w18/tree/master/lectures (also use astroML textbook)