Hierarchical models and meta-analysis;/statistics/hierarchical_metaanalysis;1
Hierarchical models;/statistics/hierarchical_models;2
Poisson regression;/statistics/poisson_regression;3
Logistic regression;/statistics/logistic_regression;4
Robust linear regression;/statistics/robust_regression;5
Multi-linear regression;/statistics/multivariate_regression;6
Linear regression with binary input;/statistics/regression_binary_input;7
Introduction to the linear regression;/statistics/regression;8
Model comparison, cont.;/statistics/model_averaging_cont;9
Model comparison;/statistics/model_averaging;10
Re-parametrizing your model;/statistics/reparametrization;11
Predictive checks;/statistics/predictive_checks;12
Trace inspection;/statistics/trace_inspection;13
Introduction to the Bayesian workflow;/statistics/bayesian_workflow;14
Mixture models;/statistics/mixture;15
Multidimensional distributions;/statistics/categories;16
The Gaussian model;/statistics/reals;17
Bonus: counting animals in a park;/statistics/hypergeom;18
The Negative Binomial model;/statistics/negbin;19
The Poisson model;/statistics/poisson;20
The Beta-Binomial model;/statistics/betabin;21
Section introduction;/statistics/simple_models_intro;22
Some notation about probability;/statistics/probability_reminder;23
How does MCMC works;/statistics/mcmc_intro;24
Introduction to Bayesian inference;/statistics/bayes_intro;25
An overview to statistics;/statistics/preface;26
The Gestalt principles;/dataviz/gestalt;27
Design tricks;/dataviz/design-introduction;28
How to choose a color map;/dataviz/palettes-introduction;29
Introduction to color perception;/dataviz/color-introduction;30
Drawing is redrawing;/dataviz/gender-economist;31
Visual queries;/dataviz/visual-queries;32
Channel effectiveness;/dataviz/effectiveness;33
Evolutions of the line chart;/dataviz/linechart-evolution;34
Beyond the 1D scatterplot;/dataviz/scatterplot-evolution;35
Perception;/dataviz/perception;36
Fundamental charts;/dataviz/fundamental-charts;37
Marks and channels;/dataviz/marks-channels;38
Data abstraction;/dataviz/data-types;39
Data visualization;/dataviz/dataviz;40

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Section introduction

Jul 24, 2024

Reading time: 1′

In the following posts we will introduce some of the most popular distributions, and we will apply them to simple problems in order to understand how to use them. We will also introduce some basic aspects of PyMC and of its ecosystem (mostly Arviz). I will assume you have some basic knowledge about probability and statistics, but no prior knowledge about Bayesian inference is required.

Stippe Jul 24, 2024