Results 11 to 20 of about 50,759 (165)
Annual Review of Statistics and Its Application, 2022 Markov chain Monte Carlo is the engine of modern Bayesian statistics, being used to approximate the posterior and derived quantities of interest. Despite this, the issue of how the output from a Markov chain is postprocessed and reported is often overlooked.
South, Leah +3 more
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Tehran Stock Exchange Return Forecasting: Comparison of Bayesian, Exponential Smoothing and Box Jenkins Approaches [PDF]
فصلنامه پژوهشهای اقتصادی ایران, 2022 Stock returns forecasting is very crucial for investors, share-holders and arbiters. Different methods have been developed for this purpose. In general, there are four methods of forecasting in stock markets, which are; Technical Analysis, Fundamental ...
Mojtaba Rostami +1 more
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Parameter Estimation in Mass Balance Model Applied in Fixed Bed Adsorption Using the Markov Chain Monte Carlo Method [PDF]
Journal of Heat and Mass Transfer Research, 2022 In this work, a mathematical model is adopted to predict the breakthrough curve in a fixed bed adsorption process, neglecting radial dispersion effects in the bed, with properties such as interstitial velocity and porosity being constant, linear ...
Rhaisa Tavares +6 more
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Journal of Open Research Software, 2021 The North American Breeding Bird Survey (BBS) is the primary ecological monitoring program used to assess the population, status, and trends of North American birds.
Brandon P. M. Edwards, Adam C. Smith
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Efficient Bayesian Structural Equation Modeling in Stan
Journal of Statistical Software, 2021 Structural equation models comprise a large class of popular statistical models, including factor analysis models, certain mixed models, and extensions thereof.
Edgar C. Merkle +3 more
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On Finite and Non-Finite Bayesian Mixture Models
Journal of New Theory, 2023 In this paper, a Bayesian paradigm of a mixture model with finite and non-finite components is expounded for a generic prior and likelihood that can be of any distributional random noise.
Sodiq Adejare Olanrewaju +3 more
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Monthly Notices of the Royal Astronomical Society, 2021 ABSTRACTWe introduce Bilby-MCMC, a Markov chain Monte Carlo sampling algorithm tuned for the analysis of gravitational waves from merging compact objects. Bilby-MCMC provides a parallel-tempered ensemble Metropolis-Hastings sampler with access to a block-updating proposal library including problem-specific and machine learning proposals. We demonstrate
G Ashton, C Talbot
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CoRR, 2020 Markov Chain Monte Carlo (MCMC) algorithms ubiquitously employ complex deterministic transformations to generate proposal points that are then filtered by the Metropolis-Hastings-Green (MHG) test. However, the condition of the target measure invariance puts restrictions on the design of these transformations.
Kirill Neklyudov, Max Welling
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Uncertainty assessment for the Bayesian updating process of concrete strength properties
Acta Polytechnica CTU Proceedings, 2022 Reassessment of infrastructure buildings has become an essential approach to deal with increasing traffic loads on ageing infrastructure buildings and to verify the service-life of those structures.
Matthias Haslbeck +3 more
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Accelerating MCMC algorithms [PDF]
WIREs Computational Statistics, 2018 Markov chain Monte Carlo algorithms are used to simulate from complex statistical distributions by way of a local exploration of these distributions. This local feature avoids heavy requests on understanding the nature of the target, but it also potentially induces a lengthy exploration of this target, with a requirement on the number of simulations ...
Robert, Christian P. +3 more
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