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Statistics

In this section I will collect all my posts about statistics.

Sometimes dataviz is not sufficient to understand your data. In this case there’s only one way you can rigorously approach your problem: statistics.
This is a huge field, and it’s quite easy to get lost within it. In the following you will find some material which helped me to clarify my ideas and to face data-related problems.


Have fun!

Stippe

Post list

Introduction
  1. An overview to statistics: What well we talk about
  2. Introduction to Bayesian inference: A little bit more about Bayesian inference
  3. How does MCMC works: Getting an idea of what's happening behind the scenes
  4. Some notation about probability: Notation and conventions
Simple models
  1. Section introduction: Understanding the building blocks
  2. The Beta-Binomial model: Dealing with binary outcomes
  3. The Poisson model: How to describe count data
  4. The Negative Binomial model: An evolution of the Poisson model
  5. Bonus: counting animals in a park: The hypergeometric distribution and the capture-mark-recapture method
  6. The Gaussian model: Handling real-valued data
  7. Multidimensional distributions: Dealing with more than two categories
  8. Mixture models: When your population is made by subpopulations
Bayesian workflow
  1. Introduction to the Bayesian workflow: How to make bayesian inference in practice
  2. Trace inspection: Finding issues in MCMC convergence
  3. Predictive checks: Verifying the predictions of your model
  4. Re-parametrizing your model: Building equivalent models with less numerical issues
  5. Model comparison: How to choose between models
  6. Model comparison, cont.: Cross validation in Bayesian statistics
Regression
  1. Introduction to the linear regression: Including dependence on external variables
  2. Linear regression with binary input: Extending regression to discrete variables
  3. Multi-linear regression: Including many covariates
  4. Robust linear regression: Reducing sensitivity to large deviations
  5. Logistic regression: How to perform regression on binary data
  6. Poisson regression: Regression on count data
Hierarchical models
  1. Hierarchical models: How to implement hierarchies
  2. Hierarchical models and meta-analysis: How hierarchical models can be used to analyze scientific literature
Advanced models
Causal inference
Time series
Non-parametric models
Other random thoughts