Posts tagged minibatch
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:
Variational Inference: Bayesian Neural Networks
- 30 May 2022
Probabilistic Programming, Deep Learning and “Big Data” are among the biggest topics in machine learning. Inside of PP, a lot of innovation is focused on making things scale using Variational Inference. In this example, I will show how to use Variational Inference in PyMC to fit a simple Bayesian Neural Network. I will also discuss how bridging Probabilistic Programming and Deep Learning can open up very interesting avenues to explore in future research.