Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Stanford Cme 106 Probability And Statistics | 165 | 3 years ago | 3 | mit | ||||||
VIP cheatsheets for Stanford's CME 106 Probability and Statistics for Engineers | ||||||||||
Teachingmaterial | 161 | 2 years ago | HTML | |||||||
Various teaching material | ||||||||||
Brainphaser | 114 | 6 years ago | 4 | gpl-3.0 | Java | |||||
Android Quiz App (Spaced Repetition) made with Material Design; features categories, statistics and different question modes | ||||||||||
Analysis Of Factorial Designs For Psychologists | 59 | a year ago | other | R | ||||||
Lesson files used in the Analysis of Factorial Designs for Psychologists. | ||||||||||
Machinelearningstatistics | 54 | 2 years ago | 1 | bsd-3-clause | Jupyter Notebook | |||||
Machine learning and statistics for physicists | ||||||||||
Bayesforundergrads | 44 | 7 years ago | mit | |||||||
Materials for a workshop on developing undergraduate classes on Bayesian statistics. | ||||||||||
Srqm | 39 | 4 years ago | 10 | TeX | ||||||
An introductory statistics course for social scientists, using Stata | ||||||||||
Stats_in_python_tutorial | 35 | 7 years ago | Python | |||||||
Material for the statistics in Python tutorial | ||||||||||
Appliedstatisticsforneuroscience | 26 | 3 years ago | 19 | Jupyter Notebook | ||||||
Materials for UC Berkeley Neuroscience 299 | ||||||||||
Computational_statistics | 24 | a day ago | 7 | gpl-3.0 | R | |||||
Course materials for Computational Statistics, PhD course at EMAp. |
Course materials for Computational Statistics, a PhD-level course at EMAp.
As complementary material,
These lecture notes by stellar statistician Susan Holmes are also well worth taking a look.
Monte Carlo theory, methods and examples by Professor Art Owen, gives a nice and complete treatment of all the topics on simulation, including a whole chapter on variance reduction.
Other materials, including lecture notes and slides may be posted here as the course progresses.
Here you can find a nascent annotated bibliography with landmark papers in the field. This review paper by Professor Hedibert Lopes is far better than anything I could conjure, however.
Books marked with [a] are advanced material.
Main
Supplementary
The two definitive texts on HMC are Neal (2011) and Betancourt (2017). A nice set of notes is Vishnoi (2021). Moreover, Hoffman & Gelman (2014) describes the No-U-turn sampler.
This post by Radford Neal explains why the Harmonic Mean Estimator (HME) is a terrible estimator of the evidence.
In these notes, Terence Tao gives insights into concentration of measure, which is the reason why integrating with respect to a probability measure in high-dimensional spaces is hard.
A Primer for the Monte Carlo Method, by the great Ilya Sobol, is one of the first texts on the Monte Carlo method.
The Harris inequality, E[fg] >= E[f]E[g]
, for f
and g
increasing, is a special case of the FKG inequality.
In Markov Chain Monte Carlo Maximum Likelihood, Charlie Geyer shows how one can use MCMC to do maximum likelihood estimation when the likelihood cannot be written in closed-form. This paper is an example of MCMC methods being used outside of Bayesian statistics.
This paper discusses the solution of Problem A in assigment 0 (2021).
Sometimes a clever way to make a target distribution easier to compute expectations with respect to is to reparametrise it. Here are some resources:
See #4. Contributed by @lucasmoschen.
In these blogs and websites you will often find interesting discussions on computational, numerical and statistical aspects of applied Statistics and Mathematics.