Results 51 to 60 of about 131,402 (244)
Bayesian Updating, Model Class Selection and Robust Stochastic Predictions of Structural Response [PDF]
A fundamental issue when predicting structural response by using mathematical models is how to treat both modeling and excitation uncertainty. A general framework for this is presented which uses probability as a multi-valued conditional logic for ...
Beck, James L.
core
BAT - The Bayesian Analysis Toolkit
We describe the development of a new toolkit for data analysis. The analysis package is based on Bayes' Theorem, and is realized with the use of Markov Chain Monte Carlo. This gives access to the full posterior probability distribution.
Akaike +14 more
core +1 more source
Abstract We establish the consistency and the asymptotic distribution of the least squares estimators of the coefficients of a subset vector autoregressive process with exogenous variables (VARX). Using a martingale central limit theorem, we derive the asymptotic normal distribution of the estimators. Diagnostic checking is discussed using kernel‐based
Pierre Duchesne +2 more
wiley +1 more source
Bayes Model Selection with Path Sampling: Factor Models and Other Examples
We prove a theorem justifying the regularity conditions which are needed for Path Sampling in Factor Models. We then show that the remaining ingredient, namely, MCMC for calculating the integrand at each point in the path, may be seriously flawed ...
Dutta, Ritabrata, Ghosh, Jayanta K.
core +1 more source
Bayesian inverse ensemble forecasting for COVID‐19
Abstract Variations in strains of COVID‐19 have a significant impact on the rate of surges and on the accuracy of forecasts of the epidemic dynamics. The primary goal for this article is to quantify the effects of varying strains of COVID‐19 on ensemble forecasts of individual “surges.” By modelling the disease dynamics with an SIR model, we solve the ...
Kimberly Kroetch, Don Estep
wiley +1 more source
Nature, Science, Bayes' Theorem, and the Whole of Reality [PDF]
A fundamental problem in science is how to make logical inferences from scientific data. Mere data does not suffice since additional information is necessary to select a domain of models or hypotheses and thus determine the likelihood of each model or ...
Alexanian, Moorad
core
In this work, we propose an improved particle swarm optimization (PSO) algorithm and develop an improved PSO‐relevance vector machine (RVM) model as a substitute for traditional true‐triaxial testing. The model's high prediction accuracy was validated through comparisons with two other machine learning methods and five three‐dimensional Hoek–Brown type
Qi Zhang +4 more
wiley +1 more source
Analysis of factors influencing hemorrhagic fever with renal syndrome and its prediction in Weifang, China from 2013 to 2021 [PDF]
Objectives This study aimed to analyze the epidemiology and trends of hemorrhagic fever with renal syndrome (HFRS) in Weifang, China (2013–2021) and to guide prevention strategies. Methods The study examined the prevalence and incidence trends of HFRS in
Hui Zhang +6 more
doaj +1 more source
Artificial intelligence in preclinical epilepsy research: Current state, potential, and challenges
Abstract Preclinical translational epilepsy research uses animal models to better understand the mechanisms underlying epilepsy and its comorbidities, as well as to analyze and develop potential treatments that may mitigate this neurological disorder and its associated conditions. Artificial intelligence (AI) has emerged as a transformative tool across
Jesús Servando Medel‐Matus +7 more
wiley +1 more source
Bayesian Probabilities and the Histories Algebra
We attempt a justification of a generalisation of the consistent histories programme using a notion of probability that is valid for all complete sets of history propositions.
A. Caticha +10 more
core +1 more source

