Results 211 to 220 of about 436,673 (317)

BiGSM: Bayesian inference of gene regulatory network via sparse modelling. [PDF]

Bioinformatics
Qin H, Garbulowski M, Sonnhammer ELL, Chatterjee S.   +3 more
europepmc   +1 more source

A hidden Markov model and reinforcement learning‐based strategy for fault‐tolerant control

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract This study introduces a data‐driven control strategy integrating hidden Markov models (HMM) and reinforcement learning (RL) to achieve resilient, fault‐tolerant operation against persistent disturbances in nonlinear chemical processes. Called hidden Markov model and reinforcement learning (HMMRL), this strategy is evaluated in two case studies
Tamera Leitao, Debaprasad Dutta, Simant R. Upreti   +2 more
wiley   +1 more source

Bayesian inference in racial health inequity analyses for noncommunicable diseases: a systematic review. [PDF]

Syst Rev
Espinosa O, Bejarano V, Mejía A, Castro H, Paternina-Caicedo A.   +4 more
europepmc   +1 more source

Restricted Tweedie stochastic block models

Canadian Journal of Statistics, EarlyView.
Abstract The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. However, conventional SBMs are not directly applicable to an adjacency matrix that consists of nonnegative zero‐inflated continuous edge weights.
Jie Jian, Mu Zhu, Peijun Sang
wiley   +1 more source

Nucleus accumbens dopamine release reflects Bayesian inference during instrumental learning. [PDF]

PLoS Comput Biol
Qü AJ, Tai LH, Hall CD, Tu EM, Eckstein MK, Mishchanchuk K, Lin WC, Chase JB, MacAskill AF, Collins AGE, Gershman SJ, Wilbrecht L.   +11 more
europepmc   +1 more source

Rank‐based estimation of propensity score weights via subclassification

Canadian Journal of Statistics, EarlyView.
Abstract Propensity score (PS) weighting estimators are widely used for causal effect estimation and enjoy desirable theoretical properties, such as consistency and potential efficiency under correct model specification. However, their performance can degrade in practice due to sensitivity to PS model misspecification.
Linbo Wang, Yuexia Zhang, Thomas S. Richardson, Xiaohua Zhou   +3 more
wiley   +1 more source

Bayesian inference of a spatially dependent semi-Markovian model with application to Madagascar Covid'19 data. [PDF]

PLoS One
Raherinirina A, Tsilefa ST, Nirilanto T, Manou-Abi SM.   +3 more
europepmc   +1 more source

Gaussian-log-Gaussian wavelet trees, frequentist and Bayesian inference, and statistical signal processing applications [PDF]

, 2014
Jesper Möller, Robert Dahl Jacobsen
openalex  

An observation‐driven state‐space model for claims size modelling

Canadian Journal of Statistics, EarlyView.
Abstract State‐space models are popular in econometrics. Recently, these models have gained some popularity in the actuarial literature. The best known state‐space models are of the Kalman‐filter type. These are called parameter‐driven because the observations do not impact the state‐space dynamics.
Jae Youn Ahn, Himchan Jeong, Mario V. Wüthrich   +2 more
wiley   +1 more source

Estimating epidemiological parameters of highly pathogenic avian influenza in common terns using exact Bayesian inference. [PDF]

J Anim Ecol
Ewing DA, Bouwhuis S.
europepmc   +1 more source