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Revisiting Bayesian Autoencoders With MCMC
IEEE Access, 2022 Autoencoders gained popularity in the deep learning revolution given their ability to compress data and provide dimensionality reduction. Although prominent deep learning methods have been used to enhance autoencoders, the need to provide robust uncertainty quantification remains a challenge. This has been addressed with variational autoencoders so far.
Rohitash Chandra +3 more
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, 2023 NESOSIM version 1.1 with modifications to enable parameter calibration with a Markov Chain Monte Carlo method. Original model by Alek Petty; modifications made by Alex Cabaj.
Alex Cabaj, Alek Petty
core +1 more source
Evaluating the Usability of a Medical Care Monitoring Center System Using Heuristic Method [PDF]
مجله انفورماتیک سلامت و زیست پزشکی, 2023 Introduction: Simultaneously with the COVID-19 epidemic, many efforts were made to manage and treat the disease. One of the main activities was the development of health information systems to help monitor this disease, but due to the speed of the ...
Zohreh Hashemi +3 more
doaj
Archives of Pharmaceutical Sciences Ain Shams University, 2022 Renal Organic Cation Transporter 2 (OCT2) plays a major role in metformin elimination. Daclatasvir, a Direct-Acting Antiviral (DAA), is an OCT2 inhibitor.
Mohamed Raslan +2 more
doaj +1 more source
Detecting recombination with MCMC [PDF]
Bioinformatics, 2002 Abstract Motivation: We present a statistical method for detecting recombination, whose objective is to accurately locate the recombinant breakpoints in DNA sequence alignments of small numbers of taxa (4 or 5). Our approach explicitly models the sequence of phylogenetic tree topologies along a multiple sequence alignment.
Dirk Husmeier, Gráinne McGuire
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MCMC‐driven importance samplers
Applied Mathematical Modelling, 2022 Monte Carlo sampling methods are the standard procedure for approximating complicated integrals of multidimensional posterior distributions in Bayesian inference. In this work, we focus on the class of Layered Adaptive Importance Sampling (LAIS) scheme, which is a family of adaptive importance samplers where Markov chain Monte Carlo algorithms are ...
F. Llorente +4 more
openaire +4 more sources
Multilevel Delayed Acceptance MCMC
SIAM/ASA Journal on Uncertainty Quantification, 2023 29 pages, 12 ...
Mikkel Bue Lykkegaard +4 more
openaire +2 more sources
Spbsampling: An R Package for Spatially Balanced Sampling
Journal of Statistical Software, 2022 The basic idea underpinning the theory of spatially balanced sampling is that units closer to each other provide less information about a target of inference than units farther apart.
Francesco Pantalone +2 more
doaj +1 more source
pexm: A JAGS Module for Applications Involving the Piecewise Exponential Distribution
Journal of Statistical Software, 2021 In this study, we present a new module built for users interested in a programming language similar to BUGS to fit a Bayesian model based on the piecewise exponential (PE) distribution.
Vinícius D. Mayrink +2 more
doaj +1 more source