MCMC

Bayesian neural networks and dimensionality reduction
In conducting non-linear dimensionality reduction and feature learning, it is common to suppose that the data lie near a …
Deborshee Sen, Theodore Papamarkou, David Dunson
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Towards efficient MCMC sampling in Bayesian neural networks by exploiting symmetry
Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density …
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
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Approximate blocked Gibbs sampling for Bayesian neural networks
In this work, minibatch MCMC sampling for feedforward neural networks is made more feasible. To this end, it is proposed to sample …
Theodore Papamarkou
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A random persistence diagram generator
Topological data analysis (TDA) studies the shape patterns of data. Persistent homology is a widely used method in TDA that summarizes …
Theodore Papamarkou, Farzana Nasrin, Austin Lawson, Na Gong, Orlando Rios, Vasileios Maroulas
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Challenges in Markov chain Monte Carlo for Bayesian neural networks
Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in Bayesian neural networks (BNNs). This paper initially reviews …
Theodore Papamarkou, Jacob Hinkle, M. Todd Young, David Womble
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Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling
The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the …
Adam Spannaus, Theodore Papamarkou, Samantha Erwin, J. Blair Christian
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Geometric adaptive Monte Carlo in random environment
Manifold Markov chain Monte Carlo algorithms have been introduced to sample more effectively from challenging target densities …
Theodore Papamarkou, Alexey Lindo, Eric B. Ford
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The efficiency of geometric samplers for exoplanet transit timing variation models
Transit timing variations (TTVs) are a valuable tool to determine the masses and orbits of transiting planets in multiplanet systems. …
Noah W. Tuchow, Eric B. Ford, Theodore Papamarkou, Alexey Lindo
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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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