Advanced Science, EarlyView.An MTJ‐based probabilistic processing‐in‐memory architecture unites nonvolatile storage, local entropy generation, and stochastic computation within each memory cell. Stored multi‐bit weights are converted directly into programmable stochastic bitstreams, enabling neural‐network inference, adaptive Ising optimization, and uncertainty‐aware Bayesian ...
Qiuyuan Wang +6 more
wiley +1 more source
Individualized Bayesian Inference Identifies Novel Genetic Variants for Parkinson's Disease. [PDF]
Genet EpidemiolRen J, Soofi YJ, Rahman MA, Lu Q, Liu J.
europepmc +1 more source
Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference
Advanced Electronic Materials, EarlyView.This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho +6 more
wiley +1 more source
Repeated exposure rivals lightweight training in Bayesian inference: accuracy, retention, and generalization. [PDF]
Front PsycholPighin S, Ghin P, Tentori K.
europepmc +1 more source
Stochastic‐MTJ Sampler Arrays for In‐Array Monte–Carlo Estimation
Advanced Electronic Materials, EarlyView.A low‐energy‐barrier magnetic tunnel junction array is operated as a probability‐domain sampler: each cell's random switching, programmed through a shared digital‐to‐analog converter, makes the per‐column multiply–accumulate an unbiased Monte–Carlo expectation estimator that returns both a mean and a calibrated uncertainty.
Ran Zhang +6 more
wiley +1 more source
Bayesian and Frequentist Inference in Partially Identified Models [PDF]
A large sample approximation of the posterior distribution of partially identified structural parameters is derived for models that can be indexed by a finite-dimensional reduced form parameter vector.
Frank Schorfheide, Hyungsik Roger Moon
core
Bayesian inference of mixed Gaussian phylogenetic models. [PDF]
BMC BioinformaticsBrahmantio B, Bartoszek K, Yapar E.
europepmc +1 more source
Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization
Advanced Electronic Materials, EarlyView.Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali +3 more
wiley +1 more source
Bayesian Model Selection in the Analysis of Cointegration [PDF]
In this paper we present the Bayesian model selection procedure within the class of cointegrated processes. In order to make inference about the cointegration space we use the class of Matrix Angular Central Gaussian distributions. To carry out posterior
Justyna Wróblewska
core
Bayesian inference captures metabolite-bacteria interactions in a microbial community. [PDF]
ISME CommunJansma J, Landi P, Hui C.
europepmc +1 more source