Posts in how-to

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.

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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.

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Censored Data Models

This example notebook on Bayesian survival analysis touches on the point of censored data. Censoring is a form of missing-data problem, in which observations greater than a certain threshold are clipped down to that threshold, or observations less than a certain threshold are clipped up to that threshold, or both. These are called right, left and interval censoring, respectively. In this example notebook we consider interval censoring.

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Model building and expansion for golf putting

top-level ‘substitutions’ key is deprecated, place under ‘myst’ key instead [myst.topmatter]

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How to wrap a JAX function for use in PyMC

top-level ‘substitutions’ key is deprecated, place under ‘myst’ key instead [myst.topmatter]

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Factor analysis

top-level ‘substitutions’ key is deprecated, place under ‘myst’ key instead [myst.topmatter]

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A Hierarchical model for Rugby prediction

top-level ‘substitutions’ key is deprecated, place under ‘myst’ key instead [myst.topmatter]

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