Bayesian Analysis of a Cointegration Model Using Markov Chain Monte Carlo
, 2017 Gael M. Martin
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Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma
CPT: Pharmacometrics &Systems Pharmacology, Volume 14, Issue 3, Page 540-550, March 2025.ABSTRACT We employed a mechanistic learning approach, integrating on‐treatment tumor kinetics (TK) modeling with various machine learning (ML) models to address the challenge of predicting post‐progression survival (PPS)—the duration from the time of documented disease progression to death—and overall survival (OS) in Head and Neck Squamous Cell ...
Kevin Atsou +4 more
wiley +1 more source
PyMC: Bayesian Stochastic Modelling in Python
Journal of Statistical Software, 2010 This user guide describes a Python package, PyMC, that allows users to efficiently code a probabilistic model and draw samples from its posterior distribution using Markov chain Monte Carlo techniques.
Anand Patil +2 more
doaj
On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo. [PDF]
PLoS Comput Biol, 2021 Rabier CE +7 more
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A Markov chain Monte Carlo analysis of the CMSSM [PDF]
, 2006 Roberto Ruiz de Austri +2 more
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An objective Bayesian method for including parameter uncertainty in ensemble model output statistics
Quarterly Journal of the Royal Meteorological Society, EarlyView.Conventional model output statistics and ensemble model output statistics methods for calibrating ensemble forecasts lead to severe underestimation of the probabilities of ensemble extremes (in blue). This is because they ignore statistical parameter uncertainty.
Stephen Jewson +4 more
wiley +1 more source
A Markov Chain Monte Carlo (MCMC) Multivariate Analysis of the Association of Vital Parameter Variation With the Lunar Cycle in Patients Hospitalized With COVID-19. [PDF]
Cureus, 2023 Koya S +3 more
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Bivariate postprocessing of wind vectors
Quarterly Journal of the Royal Meteorological Society, EarlyView.We introduce three novel bivariate postprocessing approaches and analyze their performance for joint postprocessing of bivariate wind‐vector components in Germany. Bivariate vine‐copula‐based models, a bivariate gradient‐boosted version of ensemble model output statistics (EMOS), and a bivariate distributional regression network (DRN) are compared with
Ferdinand Buchner +3 more
wiley +1 more source
Bayesian Markov Chain Monte Carlo for reparameterized Stochastic volatility models using Asian FX rates during Covid-19. [PDF]
J Appl Stat, 2023 Poonvoralak W.
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