Results 141 to 150 of about 85,479 (297)

Epistemic and aleatoric uncertainty quantification in weather and climate models

Quarterly Journal of the Royal Meteorological Society, EarlyView.
Aleatoric and epistemic uncertainties over time on weather and climate time‐scales, estimated through ensembles that sample aleatoric and epistemic uncertainty using Bayesian neural networks for parameterisations in the Lorenz 1996 model. The spread shows the 16th and 84th percentiles.
Laura A. Mansfield, Hannah M. Christensen   +1 more
wiley   +1 more source

NeuMC — A package for neural sampling for lattice field theories

SoftwareX
We present the NeuMC software package aimed at facilitating the research on neural samplers in lattice field theories. Neural samplers based on normalizing flows are becoming increasingly popular in the context of Monte–Carlo simulations as they can ...
Piotr Białas, Piotr Korcyl, Tomasz Stebel, Dawid Zapolski   +3 more
doaj   +1 more source

Parameter estimation for X-ray scattering analysis with Hamiltonian Markov Chain Monte Carlo. [PDF]

J Synchrotron Radiat, 2022
Jiang Z, Wang J, Tirrell MV, de Pablo JJ, Chen W.   +4 more
europepmc   +1 more source

Output‐Feedback H∞$$ {H}_{\infty } $$ Detector‐Based Control of Continuous‐Time Markov Jump Lur'e Systems With Sector‐Bound Optimization

International Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT This paper addresses the problem of dynamic output‐feedback H∞$$ {H}_{\infty } $$ detector‐based control for continuous‐time Markov Jump Lur'e Systems with uncertain transition rate matrices. In contrast to conventional approaches, the proposed synthesis conditions are derived using Finsler's lemma, introducing additional slack variables to ...
Lucas P. M. Silva, Cecilia F. Morais, Ricardo C. L. F. Oliveira   +2 more
wiley   +1 more source

Application of a Markov chain Monte Carlo method for robust quantification in chemical exchange saturation transfer magnetic resonance imaging. [PDF]

Quant Imaging Med Surg, 2022
Zhao Y, Wang X, Wang Y, Wang B, Zhang L, Wei X, He X.   +6 more
europepmc   +1 more source

Bayesian inference for Hidden Markov Model

Hidden Markov Models can be considered an extension of mixture models, allowing for dependent observations. In a hierarchical Bayesian framework, we show how Reversible Jump Markov Chain Monte Carlo techniques can be used to estimate the parameters of ...
Luisa Scaccia, Rosella Castellano
core  

Non‐Physical Barrier Design and Environmental Conditions Alter Routing and Survival of Juvenile Chinook Salmon (Oncorhynchus tshawytscha) in the Sacramento‐San Joaquin River Delta

River Research and Applications, EarlyView.
ABSTRACT Pacific salmon face substantial challenges when migrating through anthropogenically modified river systems, such as the Sacramento‐San Joaquin River Delta (the Delta). Non‐physical behavioral barriers, such as the bioacoustic fish fence (BAFF), are one potential solution for guiding fish away from hazards without obstructing water flow ...
Maggie Raboin, Summer M. Burdick, Russell W. Perry   +2 more
wiley   +1 more source

A multilevel Bayesian Markov Chain Monte Carlo Poisson modelling of factors associated with components of antenatal care offered to pregnant women in Nigeria. [PDF]

BMC Health Serv Res, 2023
Fagbamigbe OS, Olaseinde OS, Bello OO, Setlhare V, Nyaberi JM, Wegbom AI, Adebowale AS, Fagbamigbe AF.   +7 more
europepmc   +1 more source

Incorporating environmental DNA metabarcoding for improved benthic biodiversity and habitat mapping

Remote Sensing in Ecology and Conservation, EarlyView.
Seafloor imagery is commonly used to collect information about the distribution of benthic organisms in order to generate habitat and biodiversity maps. Recent advances in genomics (e.g., environmental DNA; eDNA) show potential to complement video surveys for habitat mapping, but there have been few examples testing this.
Rylan J. Command, Shreya Nemani, Benjamin Misiuk, Mehrdad Hajibabaei, Nicole Fahner, Emily Porter, Greg Singer, Beverly McClenaghan, Katleen Robert   +8 more
wiley   +1 more source

PyMC: Bayesian Stochastic Modelling in Python

Journal of Statistical Software, 2010
This user guide describes a Python package, PyMC, that allows users to efficiently code a probabilistic model and draw samples from its posterior distribution using Markov chain Monte Carlo techniques.
Anand Patil, David Huard, Christopher J. Fonnesbeck   +2 more
doaj