Posts in tutorial
Streaming variational inference on high-frequency tick data
- 17 August 2026
Exchanges publish per-trade archives; Binance, for instance, distributes
aggregate trades as daily and monthly files at
data.binance.vision. Widen a pull across
symbols and months and the feature matrix can outgrow the RAM of an ordinary
workstation; at that point in-memory minibatching stops being an option. PyMC’s
Minibatch() randomly slices tensor inputs; it is not itself a
disk-backed reader, so it cannot help once the array no longer fits. This
notebook fits a hierarchical hurdle–Student-t model of next-event price
moves by streaming minibatches from disk with pymc-extras’
DataLoader,
on 300,000 synthetic
rows that are generated inside the notebook. It tries to teach three things:
Forecasting Hurricane Trajectories with State Space Models
- 15 June 2025
Duplicate implicit target name: “forecasting hurricane trajectories with state space models”.
The prevalence of malaria in the Gambia
- 24 August 2024
Duplicate implicit target name: “the prevalence of malaria in the gambia”.
The Besag-York-Mollie Model for Spatial Data
- 18 August 2023
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.
Conditional Autoregressive (CAR) Models for Spatial Data
- 29 July 2022
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.
NBA Foul Analysis with Item Response Theory
- 17 April 2022
This tutorial shows an application of Bayesian Item Response Theory [Fox, 2010] to NBA basketball foul calls data using PyMC. Based on Austin Rochford’s blogpost NBA Foul Calls and Bayesian Item Response Theory.
Introduction to Bayesian A/B Testing
- 23 May 2021
This notebook demonstrates how to implement a Bayesian analysis of an A/B test. We implement the models discussed in VWO’s Bayesian A/B Testing Whitepaper [Stucchio, 2015], and discuss the effect of different prior choices for these models. This notebook does not discuss other related topics like how to choose a prior, early stopping, and power analysis.