Notebooks
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Updated
Apr 16, 2020 - Jupyter Notebook
Notebooks
Flexible Bayesian clustering framework with MCMC inference. Supports multiple nonparametric priors (DP, NGGP), distance-based models, and state-of-the-art samplers including Split-Merge algorithms. Built in C++ for efficient computations.
Codes for estimates of intensity functions parameters of multivariate counting processes and density estimation for latent variable.
Robust probabilistic inference via a constrained transport metric
Code for UAI 2025 paper: Nonparametric Bayesian Multi-Facet Clustering for Longitudinal Data
Companion R package (mvbcfMT) and reproducible code for Factorial Multivariate Bayesian Causal Forests: heterogeneous main and interaction effects of multiple treatments on correlated outcomes.
Exploring Nonparametric Bayesian Model in NLP
Quarto book reporting a preregistered Monte Carlo study of Dirichlet process mixture priors and goal-specific posterior summaries in Bayesian IRT: 120 conditions, 7,200 fits, data and code included.
Reproducibility materials for Dalla Pria, Ruggiero, Spanò, “Exact inference via quasi-conjugacy in two-parameter Poisson–Dirichlet hidden Markov models”, JASA (forthcoming). This repository includes R code, R Markdown notebooks, and cached outputs to reproduce the analyses, simulations, and figures.
Compositional Contextual Associative Bandits
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