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The Rise of Markov Chain Monte Carlo Estimation for Psychometric Modeling

Journal of Probability and Statistics, 2009
Markov chain Monte Carlo (MCMC) estimation strategies represent a powerful approach to estimation in psychometric models. Popular MCMC samplers and their alignment with Bayesian approaches to modeling are discussed.
Roy Levy
doaj   +1 more source

BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA

Barekeng
Rainfall is an important parameter in meteorology and hydrology, and it measures the amount of rain that falls from the atmosphere to the ground surface in liquid form.
Suci Astutik, Nur Silviyah Rahmi, Diego Irsandy, Fang You Dwi Ayu Shalu Saniyawati, Fidia Raaihatul Mashfia, Evelin Dewi Lusiana, Intan Fadhila Risda, Mohammad Hilmi Susanto   +7 more
doaj   +1 more source

Refinement of the Jensen integral inequality

Open Mathematics, 2016
In this paper we give a refinement of Jensen’s integral inequality and its generalization for linear functionals. We also present some applications in Information Theory.
Sever Dragomir Silvestru, Adil Khan Muhammad, Abathun Addisalem   +2 more
doaj   +1 more source

Estimate Mass Density Value as A Priori Information for Gravity by using Bayesian Markov Chain Monte Carlo (MCMC)

Indonesian Journal of Applied Physics
In the gravity method, information about mass density value is very important because it will influence the characteristic of the 1-D gravity acceleration graph.
Indriati Retno Palupi, Wiji Raharjo
doaj   +1 more source

Coupled data-driven and physical mechanism modeling of polycyclic aromatic hydrocarbon transport

Shuiwen dizhi gongcheng dizhi
Polycyclic aromatic hydrocarbons (PAHs) are among the primary organic contaminants in groundwater, and numerical modeling of PAHs transport is a crucial tool for efficient groundwater pollution remediation.
Xiankui ZENG, Jinglong TIAN, Jintong WANG, Jichun WU   +3 more
doaj   +1 more source

JaxSGMC: Modular stochastic gradient MCMC in JAX

SoftwareX
We present JaxSGMC, an application-agnostic library for stochastic gradient Markov chain Monte Carlo (SG-MCMC) in JAX. SG-MCMC schemes are uncertainty quantification (UQ) methods that scale to large datasets and high-dimensional models, enabling ...
Stephan Thaler, Paul Fuchs, Ana Cukarska, Julija Zavadlav   +3 more
doaj   +1 more source

Generative Autoencoders Coupled to Monte Carlo Simulation Allow Efficient Protein Conformation Sampling. [PDF]

J Chem Theory Comput
Beránek J, Tedeschi G, Spiwok V.
europepmc   +1 more source

Reliability, bias, and computational cost of estimating the Bayes factor using bridge sampling and the Savage-Dickey density ratio. [PDF]

Behav Res Methods
Oberauer K, Musfeld P, Aust F.
europepmc   +1 more source

Cooperative Tracking of Vessel Trajectory by Multi-Static Passive Stations Using an MC-RMPF. [PDF]

Sensors (Basel)
Liu B, Kong L, Wu P.
europepmc   +1 more source

A Step-by-Step GUI-Based Protocol for Molecular Dating Analysis Using PhyloSuite v2. [PDF]

Bio Protoc
Zhao D, Jakovlić I, Liu X, Wang S, Zhang D, Ye T.   +5 more
europepmc   +1 more source