January 2023Diffusion models II— introduces a new class of generative models using stochastic differential equations and denoising score matching.
November 2022Probabilistic reconciliation— implements and tests two recent methods on reconciliation of hierarchical time series forecasts.
June 2022Diffusion models I— introduces a novel class of generative models that are inspired by non-equilibrium thermodynamics.
January 2022Normalizing flows for variational inference— implements an *inverse autoregressive flow* for variational inference of parameters in a simple bivariate Gaussian example in Jax, Distrax, Optax and Haiku.
September 2021Tensor-product spline smoothers— implements a probabilistic model for causal inference with structured latent confounders.
August 2021VI for stick-breaking constructions— implements mean-field variational approximations for nonparametric mixture and factor models using stick-breaking constructions.
July 2021Hilbert-space approximate copula processes— explains how a copula process in conjunction with Hilbert-space approximations can be used to model stochastic volatility.
June 2021Variational LSTMs— implements a variational multivariate LSTM for timeseries prediction of an US election data set.
May 2021Hierarchical, coregionalized GPs— implements two GP models and compares their predictive performance as well as MCMC diagnostics on an US election data set.
March 2021Bayesian optimization— introduces the basics of optimization of costly to evaluate functions with probabilistic surrogate models.
October 2020Normalizing flows— shows how TensorFlow Probability can be used to implement a custom normalizing flow.
June 2020Causal structure learning using VAEs— implements a novel graph variational autoencoder and compares it to *greedy equivalence search*, one of the state-of-the-art methods for causal discovery.
January 2020Sequential regression models— introduces a special class of ordinal regression models which assume a sequential response mechanism.
October 2019Structure MCMC— shows how `PyMC3` can be used to learn the structure of a Bayesian network.
October 2019Mixed models— shows concise reference implementations for optimization of the objective of (generalized) linear mixed models.
June 2019SBC— shows a method to validate Bayesian posterior inferences.
January 2019Dirichlet process mixture models— extends the Bayesian mixture to the infinite case, i.e. when we don't know the number of clusters beforehand. We use the *Chinese restaurant process* and the *stick-breaking construction* for inference..