Results 21 to 30 of about 5,420,390 (236)

Local Search and the Evolution of World Models

Topics in Cognitive Science, EarlyView., 2023
Abstract An open question regarding how people develop their models of the world is how new candidates are generated for consideration out of infinitely many possibilities. We discuss the role that evolutionary mechanisms play in this process. Specifically, we argue that when it comes to developing a global world model, innovation is necessarily ...
Neil R. Bramley, Bonan Zhao, Tadeg Quillien, Christopher G. Lucas   +3 more
wiley   +1 more source

Uncertainty Analysis of HYDRUS-1D Model to Simulate Soil Salinity Dynamics under Saline Irrigation Water Conditions Using Markov Chain Monte Carlo Algorithm

Agronomy, 2022
Utilizing degraded quality waters such as saline water as irrigation water with proper management methods such as leaching application is a potential answer to water scarcity in agricultural systems.
Farzam Moghbel, Abolfazl Mosaedi, Jonathan Aguilar, Bijan Ghahraman, Hossein Ansari, Maria C. Gonçalves   +5 more
doaj   +1 more source

Majorize-Minimize adapted metropolis-hastings algorithm. Application to multichannel image recovery

European Signal Processing Conference, 2014
Y. Marnissi, A. Benazza-Benyahia, É. Chouzenoux, J. Pesquet   +3 more
semanticscholar   +3 more sources

Spectral gaps for a Metropolis–Hastings algorithm in infinite dimensions [PDF]

, 2011
We study the problem of sampling high and infinite dimensional target measures arising in applications such as conditioned diffusions and inverse problems.
Martin Hairer, A. Stuart, S. Vollmer
semanticscholar   +1 more source

A new adaptive approach of the Metropolis-Hastings algorithm applied to structural damage identification using time domain data

, 2020
In the present work, the formulation and solution of the inverse problem of structural damage identification is presented based on the Bayesian inference, a powerful approach that has been widely used for the formulation of inverse problems in a ...
J. S. Teixeira, L. T. Stutz, D. Knupp, A. J. S. Neto   +3 more
semanticscholar   +1 more source

Bayesian Computational Methods for Sampling from the Posterior Distribution of a Bivariate Survival Model, Based on AMH Copula in the Presence of Right-Censored Data

Entropy, 2018
In this paper, we study the performance of Bayesian computational methods to estimate the parameters of a bivariate survival model based on the Ali–Mikhail–Haq copula with marginal distributions given by Weibull distributions.
Erlandson Ferreira Saraiva, Adriano Kamimura Suzuki, Luis Aparecido Milan   +2 more
doaj   +1 more source

Using parallel computation to improve Independent Metropolis--Hastings based estimation [PDF]

, 2011
In this paper, we consider the implications of the fact that parallel raw-power can be exploited by a generic Metropolis--Hastings algorithm if the proposed values are independent.
Jacob, Pierre, Robert, Christian P., Smith, Murray H.   +2 more
core   +3 more sources

A Disease Outbreak Prediction Model Using Bayesian Inference: A Case of Influenza [PDF]

International Journal of Travel Medicine and Global Health, 2019
Introduction: One major problem in analyzing epidemic data is the lack of data and high dependency among the available data, which is due to the fact that the epidemic process is not directly observable.
Atefeh Sadat Mirarabshahi, Mehrdad Kargari   +1 more
doaj   +1 more source

Calibration and Uncertainty Analysis of Freundlich and Langmuir Isotherms Using the Markov Chain Monte Carlo (MCMC) Approach [PDF]

آب و فاضلاب
Organic pollutants, such as dyes, widely used in textile, dyeing, and chemical industries, pose significant risks to human health and the environment if introduced into water resources.
Haniyeh Sharifi Moghadam, Saeed Alimohammadi   +1 more
doaj   +1 more source

Estimation of Spatially Varying Parameters with Application to Hyperbolic SPDES

Journal of Applied Mathematics, 2023
Parameter estimation is a growing area of interest in statistical signal processing. Some parameters in real-life applications vary in space as opposed to those that are static. Most common methods in estimating parameters involve solving an optimization
David Angwenyi
doaj   +1 more source