64 citations · 130 across the 26 of their papers we have counts for
6 papers · 1 filter
A note on Bayesian R-squared for generalized additive mixed models
Abdollah Jalilian, Aki Vehtari, Luigi Sedda
We present a novel Bayesian framework to decompose the posterior predictive variance in a fitted Generalized Additive Mixed Model (GAMM) into explained and unexplained components.…
Amortized Bayesian Workflow
Chengkun Li, Aki Vehtari, Paul-Christian Bürkner +3
Bayesian inference often faces a trade-off between computational speed and sampling accuracy. We propose an adaptive workflow that integrates rapid amortized inference with gold-st…
Active Learning of Molecular Data for Task-Specific Objectives
Kunal Ghosh, Milica Todorović, Aki Vehtari +1
Active learning (AL) has shown promise for being a particularly data-efficient machine learning approach. Yet, its performance depends on the application and it is not clear when A…
posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms
Måns Magnusson, Jakob Torgander, Paul-Christian Bürkner +3
The generality and robustness of inference algorithms is critical to the success of widely used probabilistic programming languages such as Stan, PyMC, Pyro, and Turing.jl. When de…
The ARR2 prior: flexible predictive prior definition for Bayesian auto-regressions
David Kohns, Noa Kallioinen, Yann McLatchie +1
We present the ARR2 prior, a joint prior over the auto-regressive components in Bayesian time-series models and their induced . Compared to other priors designed for times-ser…
Supporting Bayesian modelling workflows with iterative filtering for multiverse analysis
Anna Elisabeth Riha, Nikolas Siccha, Antti Oulasvirta +1
When building statistical models for Bayesian data analysis tasks, required and optional iterative adjustments and different modelling choices can give rise to numerous candidate m…