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Sergio Bacallado

Researcher at University of Cambridge

Publications -  33
Citations -  1747

Sergio Bacallado is an academic researcher from University of Cambridge. The author has contributed to research in topics: Bayesian probability & Markov chain. The author has an hindex of 11, co-authored 30 publications receiving 1572 citations. Previous affiliations of Sergio Bacallado include Massachusetts Institute of Technology & Stanford University.

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Rapid equilibrium sampling initiated from nonequilibrium data

TL;DR: The adaptive seeding method (ASM), which uses nonequilibrium GE simulations to identify the metastable states, and seeds short simulations at constant temperature from each of them to quantitatively determine their equilibrium populations, and is well suited to running on modern computer clusters.
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Bayesian comparison of Markov models of molecular dynamics with detailed balance constraint

TL;DR: A Bayesian approach is proposed, which makes it possible to differentiate between models at a fixed lag time making use of short trajectories, and applies a conjugate prior for reversible Markov chains.
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Persistent Topology and Metastable State in Conformational Dynamics

TL;DR: A data-exploratory tool which provides an overview of the clustering structure under different parameters and provides a systematic way to discover clusters that are robust to perturbations of the data.