Posts in beginner
Using shared variables (Data container adaptation)
- 16 December 2021
- Category: beginner
The pymc.Data container class wraps the theano shared variable class and lets the model be aware of its inputs and outputs. This allows one to change the value of an observed variable to predict or refit on new data. All variables of this class must be declared inside a model context and specify a name for them.
Using a “black box” likelihood function (numpy)
- 16 December 2021
- Category: beginner
This notebook in part of a set of two twin notebooks that perform the exact same task, this one uses numpy whereas this other one uses Cython
Sequential Monte Carlo
- 19 October 2021
- Category: beginner
Sampling from distributions with multiple peaks with standard MCMC methods can be difficult, if not impossible, as the Markov chain often gets stuck in either of the minima. A Sequential Monte Carlo sampler (SMC) is a way to ameliorate this problem.
Splines in PyMC3
- 08 October 2021
- Category: beginner
Often, the model we want to fit is not a perfect line between some \(x\) and \(y\). Instead, the parameters of the model are expected to vary over \(x\). There are multiple ways to handle this situation, one of which is to fit a spline. The spline is effectively multiple individual lines, each fit to a different section of \(x\), that are tied together at their boundaries, often called knots. Below is an exmaple of how to fit a spline using the Bayesian framework PyMC3.
Multivariate Gaussian Random Walk
- 25 September 2021
- Category: beginner
This notebook shows how to fit a correlated time series using multivariate Gaussian random walks (GRWs). In particular, we perform a Bayesian regression of the time series data against a model dependent on GRWs.