Results 71 to 80 of about 132,359 (288)
Multimodal parameter spaces of a complex multi-channel neuron model
Frontiers in Systems Neuroscience, 2022 One of the most common types of models that helps us to understand neuron behavior is based on the Hodgkin–Huxley ion channel formulation (HH model). A major challenge with inferring parameters in HH models is non-uniqueness: many different sets of ion ...
Y. Curtis Wang +10 more
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Forecasting Related Time Series
Journal of Applied Econometrics, EarlyView.ABSTRACT A collection of time series are “related” if they follow similar stochastic processes and/or they are statistically dependent. This paper proposes a related time series (RTS) forecasting model that exploits these relationships. The model's foundation is a set of univariate Gaussian autoregressions, one for each series, which are then augmented
Ulrich K. Müller, Mark W. Watson
wiley +1 more source
Application of Markov chain Monte carlo method in Bayesian statistics
MATEC Web of Conferences, 2016 In statistical inference methods, bayesian method is a method of great influence. This paper introduces the basic idea of the bayesian method. However, the widespread popularity of MCMC samplers is largely due to their impact on solving statistical ...
Zhao Qi
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Bayesian computation for statistical models with intractable normalizing constants [PDF]
, 2008 This paper deals with some computational aspects in the Bayesian analysis of statistical models with intractable normalizing constants. In the presence of intractable normalizing constants in the likelihood function, traditional MCMC methods cannot be ...
Atchade, Yves +2 more
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Partition MCMC for inference on acyclic digraphs
, 2015 Acyclic digraphs are the underlying representation of Bayesian networks, a widely used class of probabilistic graphical models. Learning the underlying graph from data is a way of gaining insights about the structural properties of a domain.
Kuipers, Jack, Moffa, Giusi
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Advanced MCMC methods for sampling on diffusion pathspace
Stochastic Processes and their Applications, 2013 The need to calibrate increasingly complex statistical models requires a persistent effort for further advances on available, computationally intensive Monte Carlo methods. We study here an advanced version of familiar Markov Chain Monte Carlo (MCMC) algorithms that sample from target distributions defined as change of measures from Gaussian laws on ...
Beskos, Alexandros +2 more
openaire +4 more sources
Monetary Policy Shocks and Exchange Rate Dynamics in Small Open Economies
Journal of Applied Econometrics, EarlyView.ABSTRACT This paper investigates whether the effects of monetary policy shocks on real exchange rates have changed over time and, if so, whether these changes stem from shifts in transmission mechanisms or from variation in the volatility of the shocks themselves.
Madison Terrell +3 more
wiley +1 more source
Evaluation of fall‐seeded cover crops for grassland nesting waterfowl in eastern South Dakota
Wildlife Society Bulletin, EarlyView., 2023 Cover crops are experiencing a revival among Midwestern farmers, and we assessed their attractiveness and safety for nesting ducks in South Dakota. Nest success was markedly lower in cover crops than in perennial cover during both years of our study, including 2019 which was a best‐case scenario for cover crops, with extremely wet conditions delaying ...
Charles W. Gallman +3 more
wiley +1 more source
A comparison of Bayesian and Fourier methods for frequency determination in asteroseismology
, 2010 Bayesian methods are becoming more widely used in asteroseismic analysis. In particular, they are being used to determine oscillation frequencies, which are also commonly found by Fourier analysis.
Bedding, Timothy R. +4 more
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To MCMC or not to MCMC: Evaluating non-MCMC methods for Bayesian penalized regression
Markov Chain Monte Carlo (MCMC) sampling is computationally expensive, especially for complex models. Alternative methods make simplifying assumptions about the posterior to reduce computational burden, but their impact on predictive performance remains unclear.
van Leeuwen, Florian D., van Erp, Sara
openaire +2 more sources