MCMC

Bayesian random persistence diagram generation: an application to material microstructure analysis
Data analysis helps identify changes in the microstructure of materials, but is often hindered by the cost and time requirements of …
Farzana Nasrin, Theodore Papamarkou, Austin Lawson, Na Gong, Orlando Rios, Vasileios Maroulas
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Connecting the dots: is mode-connectedness the key to feasible sample-based inference in Bayesian neural networks?
A major challenge in sample-based inference (SBI) for Bayesian neural networks is the size and structure of the networks’ parameter …
Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer
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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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