Note 1: Mathematica 11+ is recommended, but probably older versions will work as well. Read the paper and download the codes here.ġ5) The website of the Effective Fluid CLASS (EFCLASS) code, based on the papers and, can be found here. (obsolete)ĩ) A Mathematica Interface for CosmoMC, go here.ġ0a) Fitting the Union 2.1 SnIa data (standard) Download.ġ0b) Fitting the Union 2.1 SnIa data ( ultra-fast) Download.ġ0c) Fitting the JLA SnIa data ( 7zip format) Download.ġ0d) Fitting the Pantheon SnIa data Download.ġ1) Joint SnIa, CMB, BAO and growth-rate likelihood! ( ultra-fast) Download.ġ2) Parallelization CPU/GPU examples(coming soon).ġ3) The CMB power spectrum and the cosmological parameters the correlation function (no RSD) Download.ġ4) The spectrum of the Gravitational Wave (GW) emission from a hyperbolic orbit. Download.Ħb) Explaining the 1/e percentage replacement rule of the Bootstrap. Download.ĥ) Generic Markov Chain Monte Carlo v2 (MCMC). Download.Ĥ) Generic Markov Chain Monte Carlo (MCMC). Download.ģb) Contours from empirical distribution, eg an MCMC etc. Download.ģa) Data fitting, contours, error bars etc. Download.Ģc) The large N limit of the Poisson distribution (-> Gaussian). Download.Ģb) Marginalization of block covariance matrices. Download.Ģa) Stuff about covariance matrices. Numerical codes: (right-click on "Download" and hit "Save as")ġa) Statistical Significance and Sigmas. Gfortran 4.5+ is highly recommended.Ĥ) Run the codes and try to understand what's going on and most importantly why.
Ģ) Download the Mathematica codes found below and that illustrate several key issues, like minimization and basic statistical analysis, contours, MCMC, Fourier analysis, parallelization (CPU/GPU) etc.ģ) Get CAMB from here and follow the instructions in the Readme to compile and install it. My new RSD growth-rate likelihood for Montepython can be found at my GitHub repository RSD-growth.ġ) Study Chapter 15 of Numerical Recipes regarding data-fitting, minimization, MCMC, statistics etc, see also. Note: for my CMB part of the CoAv course, go here.
However, some of the examples might be a bit more advanced. Most of the following material is intended for master and fledgling PhD students who want to understand the basics of data analysis with a focus on cosmology and want to enter the world of research. In this part I will try to explain several key issues in data analysis and statistics with the use of explicit examples and numerical codes.
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