Posts in advanced
Discrete Choice and Random Utility Models
This notebook uses libraries that are not PyMC dependencies and therefore need to be installed specifically to run this notebook. Open the dropdown below for extra guidance.
Longitudinal Models of Change
The study of change involves simultaneously analysing the individual trajectories of change and abstracting over the set of individuals studied to extract broader insight about the nature of the change in question. As such it’s easy to lose sight of the forest for the focus on the trees. In this example we’ll demonstrate some of the subtleties of using hierarchical bayesian models to study the change within a population of individuals - moving from the within individual view to the between/cross individuals perspective.
Bayesian Missing Data Imputation
- 08 February 2023
- advanced
Duplicate implicit target name: “bayesian missing data imputation”.
Pathfinder Variational Inference
Pathfinder [Zhang et al., 2021] is a variational inference algorithm that produces samples from the posterior of a Bayesian model. It compares favorably to the widely used ADVI algorithm. On large problems, it should scale better than most MCMC algorithms, including dynamic HMC (i.e. NUTS), at the cost of a more biased estimate of the posterior. For details on the algorithm, see the arxiv preprint.
Fitting a Reinforcement Learning Model to Behavioral Data with PyMC
Reinforcement Learning models are commonly used in behavioral research to model how animals and humans learn, in situtions where they get to make repeated choices that are followed by some form of feedback, such as a reward or a punishment.
Gaussian Processes using numpy kernel
- 31 July 2022
- advanced
Example of simple Gaussian Process fit, adapted from Stan’s example-models repository.
How to wrap a JAX function for use in PyMC
This notebook uses libraries that are not PyMC dependencies and therefore need to be installed specifically to run this notebook. Open the dropdown below for extra guidance.
Factor analysis
Factor analysis is a widely used probabilistic model for identifying low-rank structure in multivariate data as encoded in latent variables. It is very closely related to principal components analysis, and differs only in the prior distributions assumed for these latent variables. It is also a good example of a linear Gaussian model as it can be described entirely as a linear transformation of underlying Gaussian variates. For a high-level view of how factor analysis relates to other models, you can check out this diagram originally published by Ghahramani and Roweis.
Dirichlet mixtures of multinomials
- 08 January 2022
- advanced
This example notebook demonstrates the use of a Dirichlet mixture of multinomials (a.k.a Dirichlet-multinomial or DM) to model categorical count data. Models like this one are important in a variety of areas, including natural language processing, ecology, bioinformatics, and more.
Dirichlet process mixtures for density estimation
- 16 September 2021
- advanced
The Dirichlet process is a flexible probability distribution over the space of distributions. Most generally, a probability distribution, \(P\), on a set \(\Omega\) is a [measure](https://en.wikipedia.org/wiki/Measure_(mathematics%29) that assigns measure one to the entire space (\(P(\Omega) = 1\)). A Dirichlet process \(P \sim \textrm{DP}(\alpha, P_0)\) is a measure that has the property that, for every finite disjoint partition \(S_1, \ldots, S_n\) of \(\Omega\),
About Conditional Autoregressive models in PyMC
- 14 August 2020
- advanced, explanation
This notebook explains the design principles behind the Conditional Autoregressive (CAR) distribution as implemented in PyMC. For a simple tutorial of why and how to use the CAR distribution, see this notebook.