Results 141 to 150 of about 44,986 (305)
Adaptive Gibbs samplers and related MCMC methods [PDF]
, 2011 We consider various versions of adaptive Gibbs and Metropolis- within-Gibbs samplers, which update their selection probabilities (and perhaps also their proposal distributions) on the y during a run, by learning as they go in an attempt to optimise ...
Rosenthal, Jeffrey S. (Jeffrey Seth) +2 more
core
Accuracy of occurrence and abundance estimates from insect metabarcoding
Methods in Ecology and Evolution, EarlyView.Abstract DNA metabarcoding—high‐throughput sequencing of barcode regions from bulk samples—has become a key tool for insect biodiversity assessment. Yet, how methodological choices affect the accuracy of metabarcoding data remains insufficiently explored. In this paper, we ask: (1) How does the lysis method (non‐destructive lysis vs.
Ela Iwaszkiewicz‐Eggebrecht +12 more
wiley +1 more source
Phase randomisation: a convergence diagnostic test for MCMC
Most MCMC users address the convergence problem by applying diagnostic tools to the output produced by running their samplers. Potentially useful diagnostics may be borrowed from diverse areas such as time series.
Rodney C Wolff +2 more
core
, 2016 Generalized linear mixed regression model for changes in number of viable fleas calculated using a Monte-Carlo-Markov-Chain (MCMC) approach.
Jürgen Krücken (225269) +5 more
core +1 more source
BIMr2: Inference of migration rates and their drivers from multilocus genotypes
Methods in Ecology and Evolution, EarlyView.Abstract Understanding how environmental factors drive recent migration is crucial for predicting species' responses to habitat change and for informing conservation strategies. There is one method available (BIMr) aimed at identifying the environmental factors associated with migration patterns using genetic data.
Igor J. Chybicki +2 more
wiley +1 more source
Efficient MCMC and posterior consistency for Bayesian inverse problems [PDF]
Many mathematical models used in science and technology often contain parameters that are not known a priori. In order to match a model to a physical phenomenon, the parameters have to be adapted on the basis of the available data.
Vollmer, Sebastian
core
Aprendizagem estrutural de redes bayesianas pelo método de Monte Carlo e cadeias de Markov [PDF]
, 2013 Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2013.Esta dissertação aborda a aplicação dos métodos de Monte Carlo via Cadeias de Markov na ...
Costa, Felipe Schneider
core
, 2016 Generalized linear mixed regression model for changes in the severity score for acute pig tungiasis (SSAPT) calculated using a Monte-Carlo-Markov-Chain (MCMC) approach.
Jürgen Krücken (225269) +5 more
core +1 more source
Monetary Policy, Investor Sentiment and Stock Price Bubble: Evidence From China
Accounting &Finance, EarlyView.ABSTRACT The empirical results indicate that an increase in interest rates may stimulate a significant and persistent stock price bubble, which is consistent with rational asset price bubble theory. This finding suggests that central banks should implement anti‐turbulent monetary policy with caution, since inappropriate tightening may unintentionally ...
Jiahao Gong +3 more
wiley +1 more source