Detecting neutron sources by borrowing inference tools from cosmology

Neutron sources can be directly identified from measured spectra rather than proxies using inference tools adapted from ...
IEEE

Novel Bayesian Inference Algorithms for Multiuser Detection in M2M Communications

Abstract: The fifth generation and future wireless networks are expected to support massive machine-to-machine (M2M) communications. Due to the sporadic nature, massive M2M communications can be well ...
leewayhertz

Bayesian networks in AI: Role in machine learning, example, types and applications

Imagine a scenario where a team of doctors faces a perplexing medical puzzle. A patient shows a range of symptoms, each pointing to multiple possible diseases. How can they navigate this diagnostic ...
Bloomberg L.P.

Introducing the Seventh Cohort of Bloomberg Data Science Ph.D. Fellows (2024-2025)

Bloomberg is pleased to announce the newest cohort of three early-career researchers who have received the Bloomberg Data Science Ph.D. Fellowship for 2024-2025. Now in its seventh cohort, the ...
IEEE

Variational Bayesian Inference Algorithms for Infinite Relational Model of Network Data

Abstract: Network data show the relationship among one kind of objects, such as social networks and hyperlinks on the Web. Many statistical models have been proposed for analyzing these data. For ...
GitHub

PyVBMC: Variational Bayesian Monte Carlo in Python

PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference, previously implemented in MATLAB. VBMC is an approximate inference method ...
PNAS

Efficient Bayesian inference under the multispecies coalescent with migration

Inference of gene flow using genomic data requires powerful methods as the process of coalescent, migration, and mutation is highly stochastic. However, it is challenging to implement the multispecies ...
PNAS

Bayesian interpolation with deep linear networks

Understanding the interplay between network architecture, dataset statistics, and learning algorithms is a key challenge in deep learning. We overcome this challenge analytically for zero-noise ...
Frontiers

Bayesian Pure-Tone Audiometry Through Active Learning Under Informed Priors

Pure-tone audiometry—the process of estimating a person's hearing threshold from “audible” and “inaudible” responses to tones of varying frequency and intensity—is the basis for diagnosing and ...