Theodore Papamarkou
Theodore Papamarkou
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MCMC
Multiphase MCMC sampling for parameter inference in nonlinear ordinary differential equations
Traditionally, ODE parameter inference relies on solving the system of ODEs and assessing fit of the estimated signal with the …
Alan Lazarus
,
Dirk Husmeier
,
Theodore Papamarkou
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The controlled thermodynamic integral for Bayesian model evidence evaluation
Approximation of the model evidence is well known to be challenging. One promising approach is based on thermodynamic integration, but …
Chris J. Oates
,
Theodore Papamarkou
,
Mark Girolami
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RNA editing generates cellular subsets with diverse sequence within populations
RNA editing is a mutational mechanism that specifically alters the nucleotide content in transcribed RNA. However, editing rates vary …
Dewi Harjanto
,
Theodore Papamarkou
,
Chris J. Oates
,
Violeta Rayon-Estrada
,
F. Nina Papavasiliou
,
Anastasia Papavasiliou
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EWS-FLI1 employs an E2F switch to drive target gene expression
Cell cycle progression is orchestrated by E2F factors. We previously reported that in ETS-driven cancers of the bone and prostate, …
Raphaela Schwentner
,
Theodore Papamarkou
,
Maximilian O. Kauer
,
Vassilios Stathopoulos
,
Fan Yang
,
Sven Bilke
,
Paul S. Meltzer
,
Mark Girolami
,
Heinrich Kovar
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Monte Carlo methods and zero variance principle
The principle dates back to 1999, when it was introduced in the physics literature by [3]. The physical nomenclature would broadly …
Theodore Papamarkou
,
Antonietta Mira
,
Mark Girolami
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Zero variance differential geometric Markov chain Monte Carlo algorithms
Differential geometric Markov Chain Monte Carlo (MCMC) strategies exploit the geometry of the target to achieve convergence in fewer …
Theodore Papamarkou
,
Antonietta Mira
,
Mark Girolami
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