Results 71 to 80 of about 168,901 (276)
sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo
Journal of Statistical Software, 2019 This paper introduces the R package sgmcmc; which can be used for Bayesian inference on problems with large data sets using stochastic gradient Markov chain Monte Carlo (SGMCMC).
Jack Baker +3 more
doaj +1 more source
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
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
Credit risk clustering in a business group: Which matters more, systematic or idiosyncratic risk?
Cogent Economics & Finance, 2019 Understanding how defaults correlate across firms is a persistent concern in risk management. In this paper, we apply covariate-dependent copula models to assess the dynamic nature of credit risk dependence, which we define as “credit risk clustering ...
Feng Li, Zhuojing He
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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
Bayesian parameter inference by Markov chain Monte Carlo with hybrid fitness measures: theory and test in apoptosis signal transduction network. [PDF]
PLoS ONE, 2013 When model parameters in systems biology are not available from experiments, they need to be inferred so that the resulting simulation reproduces the experimentally known phenomena. For the purpose, Bayesian statistics with Markov chain Monte Carlo (MCMC)
Yohei Murakami, Shoji Takada
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JCPP Advances, EarlyView.Abstract Background Adolescence is marked by increased vulnerability to sleep disturbances and mood disorders. Understanding how day‐to‐day changes in sleep and mood are linked within the same individual is crucial for clarifying sleep's role in emerging internalizing disorders. However, the extent to which an adolescent's fluctuations in sleep predict
Konstantin Drexl +4 more
wiley +1 more source
Why Bayesian Ideas Should Be Introduced in the Statistics Curricula and How to Do So
Journal of Statistics Education, 2020 While computing has become an important part of the statistics field, course offerings are still influenced by a legacy of mathematically centric thinking.
Andrew Hoegh
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ParaHox Genes Revisited: From Gut Patterning to Integrated Axial and Neural Organization in Rotifera
Journal of Experimental Zoology Part B: Molecular and Developmental Evolution, EarlyView.In rotifers, ParaHox genes show a dispersed genomic organization, with Xlox absent across gnathiferans. Exclusive neuronal expression of Gsx and Cdx reveals that ancestral ParaHox genes coordinated neural and epithelial development beyond gut patterning, suggesting an integrated role in early bilaterian body plan organization.
Andreas C. Fröbius +2 more
wiley +1 more source
MCMC Using Hamiltonian Dynamics [PDF]
, 2011 Hamiltonian dynamics can be used to produce distant proposals for the Metropolis algorithm, thereby avoiding the slow exploration of the state space that results from the diffusive behaviour of simple random-walk proposals. Though originating in physics, Hamiltonian dynamics can be applied to most problems with continuous state spaces by simply ...
openaire +2 more sources