A multilevel Bayesian Markov Chain Monte Carlo Poisson modelling of factors associated with components of antenatal care offered to pregnant women in Nigeria. [PDF]
BMC Health Serv Res, 2023 Fagbamigbe OS +7 more
europepmc +1 more source
Grey-Box Energy Modelling of Energy-Efficient House Using Hybrid Optimization Technique of Genetic Algorithms (GA) and Quasi-Newton Algorithms with Markov Chain Monte Carlo Uncertainty Distribution [PDF]
Gulsun Demirezen +2 more
openalex +1 more source
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
International Conference on Machine Learning, 2008 R. Salakhutdinov, A. Mnih
semanticscholar +1 more source
CPT: Pharmacometrics &Systems Pharmacology, Volume 14, Issue 3, Page 486-494, March 2025.Abstract The pharmaceutical industry has increasingly adopted model‐informed drug discovery and development (MID3) to enhance productivity in drug discovery and development. Quantitative systems pharmacology (QSP), which integrates drug action mechanisms and disease complexities to predict clinical endpoints and biomarkers is central to MID3.
Hiroaki Iwata, Ryuta Saito
wiley +1 more source
An Auxiliary Variable Method for Markov Chain Monte Carlo Algorithms in High Dimension [PDF]
, 2018 Yosra Marnissi +3 more
openalex +1 more source
Particle Markov chain Monte Carlo methods
, 2010 C. Andrieu, A. Doucet, R. Holenstein
semanticscholar +1 more source
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
europepmc +1 more source
Markov Chain Monte Carlo Posterior Density Approximation for a Groove-Dimensioning Purpose
, 2006 José Ismael De la Rosa Vargas +3 more
openalex +2 more sources