Bayesian Learning
Entropy testing and its application to testing Bayesian networks
This paper studies the problem of entropy identity testing: given sample access to a distribution p and a fully described distribution q (both discrete distributions over a domain of size k), and the promise that either p = q or |H (p) H (q)| ฮต, where H () denotes the Shannon entropy, a tester needs to distinguish between the two cases with high probability.
Inference of Neural Dynamics Using Switching Recurrent Neural Networks
SRNNs are able to automatically identify discrete states with distinct nonlinear neural dynamics.
On Causal Discovery in the Presence of Deterministic Relations
In this paper, we find, supported by both theoretical analysis and empirical evidence, that score-based methods with exact search can naturally address the issues of deterministic relations under rather mild assumptions. Nonetheless, exact score-based methods can be computationally expensive.
Block Sparse Bayesian Learning: A Diversified Scheme
This paper introduces a novel prior called Diversified Block Sparse Prior to characterize the widespread block sparsity phenomenon in real-world data.
Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference
ASNPE brings an active learning scheme into the inference loop to estimate the utility of simulation parameter candidates to the underlying probabilistic model.