Results 81 to 90 of about 2,604,808 (287)
Microclimate mediates the strength and direction of avian biotic interactions
Ecography, EarlyView.Theory predicts that the strength and direction of species interactions can shift from being competitive in benign environments toward being facilitative in stressful environments. However, the environmental context dependency of species interactions has rarely been tested in animal communities.
Sarah J. K. Frey +6 more
wiley +1 more source
A Bayesian model for binary Markov chains
International Journal of Mathematics and Mathematical Sciences, 2004 This note is concerned with Bayesian estimation of the transition probabilities of a binary Markov chain observed from heterogeneous individuals. The model is founded on the Jeffreys' prior which allows for transition probabilities to be correlated.
Souad Assoudou, Belkheir Essebbar
doaj +1 more source
Computing the Cramer-Rao bound of Markov random field parameters: Application to the Ising and the Potts models [PDF]
, 2013 This letter considers the problem of computing the Cramer–Rao bound for the parameters of a Markov random field. Computation of the exact bound is not feasible for most fields of interest because their likelihoods are intractable and have intractable ...
Batatia, Hadj +8 more
core +1 more source
Ecography, EarlyView.Animal coloration exhibits substantial biogeographic variation, reflecting complex adaptations to both abiotic and biotic environments. Although the evolutionary mechanisms underlying avian plumage coloration have been widely studied, geographic variation in plumage patterning has received far less attention.
Yuqing Han +8 more
wiley +1 more source
, 2020 Probabilistic models used for forecasting rainfall can help stakeholders in improving crop productivity through better utilization and preplanning of water resources, as it is the crucial element of major decisions because of the dynamic nature of ...
Deo, Ravinesh C. +3 more
core +1 more source
Identifying influential observations in Bayesian models by using Markov chain Monte Carlo. [PDF]
, 2011 In statistical modelling, it is often important to know how much parameter estimates are influenced by particular observations. An attractive approach is to re-estimate the parameters with each observation deleted in turn, but this is computationally ...
James Carpenter +5 more
core +1 more source
Generative Models in Inorganic Crystals Discovery and Inverse Design
ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.Generative inverse‐design samples from the vast inorganic crystal design space by starting from target properties such as band gap, stability, and ion transport. This Review examines the representations, generative models, and validation workflows needed to translate candidate structures into stable, potentially synthesizable materials for applications
Tao Li +5 more
wiley +1 more source
Genetics Selection Evolution, 2019 Background Hamiltonian Monte Carlo is one of the algorithms of the Markov chain Monte Carlo method that uses Hamiltonian dynamics to propose samples that follow a target distribution.
Motohide Nishio, Aisaku Arakawa
doaj +1 more source
Zero variance in Markov chain Monte Carlo with an application to credit risk estimation [PDF]
We propose a general purpose variance reduction technique for Markov Chain Monte Carlo estimators based on the Zero-Variance principle introduced in the physics lit- erature by Assaraf and Caarel ( 1999). The potential of the new idea is illustrated with
Tenconi Paolo
core
Quantitative non-geometric convergence bounds for independence samplers [PDF]
, 2009 Markov chain Monte Carlo (MCMC) algorithms are widely used in statistics, physics, and computer science, to sample from complicated high-dimensional probability distributions.
Rosenthal, Jeffrey S. (Jeffrey Seth) +1 more
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