Results 121 to 130 of about 147,580 (306)
Bayesian inference for the learning rate in Generalised Bayesian inference
In Generalised Bayesian Inference (GBI), the learning rate and hyperparameters of the loss must be estimated. However, these inference-hyperparameters can't be estimated jointly with the other parameters by giving them a prior, as we discuss. Several methods for estimating the learning rate have been given which elicit and minimise a loss based on the ...
Lee, Jeong Eun +2 more
openaire +2 more sources
Robust Bayesian inference in Iq-Spherical models [PDF]
, 1992 The class of multivariate lq-spherical distributions is introduced and defined through their isodensity surfaces. We prove that, under a Jeffreys' type improper prior on the scale parameter, posterior inference on the location parameters is the same for
Osiewalski, Jacek, Steel, Mark F.J.
core
Advanced Science, EarlyView.Phylogenetic and biochemical analyses of the heme transporter CydDC reveal its functional conservation throughout bacterial evolution and demonstrate its unique asymmetric allosteric mechanism. Furthermore, impairment of CydDC function directly affects bacterial antibiotic resistance and likely compromises antibiotic efficacy through drug efflux.
Lili Yang +19 more
wiley +1 more source
A Bayesian perspective on observers’ inference of group norms
npj Science of LearningInferring group norms is crucial for adapting behaviors in novel situations, but its underlying basis and computational account remain unclear. This study manipulated the prevalence of norm-consistent behaviors (i.e., straight-line movements) to examine ...
Jipeng Duan +3 more
doaj +1 more source
Application of Markov chain Monte carlo method in Bayesian statistics
MATEC Web of Conferences, 2016 In statistical inference methods, bayesian method is a method of great influence. This paper introduces the basic idea of the bayesian method. However, the widespread popularity of MCMC samplers is largely due to their impact on solving statistical ...
Zhao Qi
doaj +1 more source
On Bayesian inference with conjugate priors for scale mixtures of normal distributions
, 2010 Bayesian inference is considered for the multivariate regression model with distribution of the random responses belonging to the multivariate scale mixtures of normal distributions.
Ng, V.M.
core
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
Bayesian Semiparametric Inference in Multiple Equation Models [PDF]
This paper outlines an approach to Bayesian semiparametric regression in multiple equation models which can be used to carry out inference in seemingly unrelated regressions or simultaneous equations models with nonparametric components.
Gary Koop, Dale Poirier, Justin Tobias
core
Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories
Advanced Energy Materials, EarlyView.In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen +4 more
wiley +1 more source
Advanced Energy Materials, EarlyView.We identify two decisive levers for SAM interfaces: molecular design (carboxylic acid‐based, phosphonic acid, other anchoring chemistries, and polymeric SAMs) and mixing routes (co‐assembly, in situ assembly, pre‐ and post‐treatment). Coordinated tuning of headgroups and assembly pathways optimises energy alignment and film formation, suppresses ...
Jiaxu Zhang, Bochun Kang, Feng Yan
wiley +1 more source