Search Results - "Johansen, Adam M"

Toward Automatic Model Comparison: An Adaptive Sequential Monte Carlo Approach by Zhou, Yan, Johansen, Adam M., Aston, John A.D.

“…Model comparison for the purposes of selection, averaging, and validation is a problem found throughout statistics. Within the Bayesian paradigm, these…”
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A spatio-temporal model to reveal oscillator phenotypes in molecular clocks: Parameter estimation elucidates circadian gene transcription dynamics in single-cells by Unosson, Måns, Brancaccio, Marco, Hastings, Michael, Johansen, Adam M, Finkenstädt, Bärbel

“…We propose a stochastic distributed delay model together with a Markov random field prior and a measurement model for bioluminescence-reporting to analyse…”
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Microtubule organization within mitotic spindles revealed by serial block face scanning electron microscopy and image analysis by Nixon, Faye M, Honnor, Thomas R, Clarke, Nicholas I, Starling, Georgina P, Beckett, Alison J, Johansen, Adam M, Brettschneider, Julia A, Prior, Ian A, Royle, Stephen J

“…Serial block face scanning electron microscopy (SBF-SEM) is a powerful method to analyze cells in 3D. Here, working at the resolution limit of the method, we…”
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The Iterated Auxiliary Particle Filter by Guarniero, Pieralberto, Johansen, Adam M., Lee, Anthony

“…We present an offline, iterated particle filter to facilitate statistical inference in general state space hidden Markov models. Given a model and a sequence…”
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Single-molecule level analysis of the subunit composition of the T cell receptor on live T cells by James, John R, White, Samuel S, Clarke, Richard W, Johansen, Adam M, Dunne, Paul D, Sleep, David L, Fitzgerald, William J, Davis, Simon J, Klenerman, David

“…The T cell receptor (TCR) expressed on most T cells is a protein complex consisting of TCRαβ heterodimers that bind antigen and cluster of differentiation (CD)…”
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Global Consensus Monte Carlo by Rendell, Lewis J., Johansen, Adam M., Lee, Anthony, Whiteley, Nick

“…To conduct Bayesian inference with large datasets, it is often convenient or necessary to distribute the data across multiple machines. We consider a…”
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On the exact and $\varepsilon$-strong simulation of (jump) diffusions by Pollock, Murray, Johansen, Adam M., Roberts, Gareth O.

“…This paper introduces a framework for simulating finite dimensional representations of (jump) diffusion sample paths over finite intervals, without…”
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Static-parameter estimation in piecewise deterministic processes using particle Gibbs samplers by Finke, Axel, Johansen, Adam M., Spanò, Dario

“…We develop particle Gibbs samplers for static-parameter estimation in discretely observed piecewise deterministic process (PDPs). PDPs are stochastic processes…”
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A Particle Method for Solving Fredholm Equations of the First Kind by Crucinio, Francesca R., Doucet, Arnaud, Johansen, Adam M.

“…Fredholm integral equations of the first kind are the prototypical example of ill-posed linear inverse problems. They model, among other things, reconstruction…”
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The node-wise Pseudo-marginal method: model selection with spatial dependence on latent graphs by Thesingarajah, Denishrouf, Johansen, Adam M.

“…Motivated by problems from neuroimaging in which existing approaches make use of “mass univariate” analysis which neglects spatial structure entirely, but the…”
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Limit theorems for sequential MCMC methods by Finke, Axel, Doucet, Arnaud, Johansen, Adam M.

“…Both sequential Monte Carlo (SMC) methods (a.k.a. ‘particle filters’) and sequential Markov chain Monte Carlo (sequential MCMC) methods constitute classes of…”
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Erratum: Asymptotic genealogies of interacting particle systems with an application to sequential Monte Carlo by Koskela, Jere, Jenkins, Paul A., Johansen, Adam M., Spanò, Dario

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Quasi‐stationary Monte Carlo and the ScaLE algorithm by Pollock, Murray, Fearnhead, Paul, Johansen, Adam M., Roberts, Gareth O.

“…Summary This paper introduces a class of Monte Carlo algorithms which are based on the simulation of a Markov process whose quasi‐stationary distribution…”
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The divide-and-conquer sequential Monte Carlo algorithm: Theoretical properties and limit theorems by Kuntz, Juan, Crucinio, Francesca R., Johansen, Adam M.

“…We provide a comprehensive characterisation of the theoretical properties of the divide-and-conquer sequential Monte Carlo (DaC-SMC) algorithm. We firmly…”
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Cost free hyper-parameter selection/averaging for Bayesian inverse problems with vanilla and Rao-Blackwellized SMC samplers by Viani, Alessandro, Johansen, Adam M., Sorrentino, Alberto

“…In Bayesian inverse problems, one aims at characterizing the posterior distribution of a set of unknowns, given indirect measurements. For…”
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Unbiased Simulation of Rare Events in Continuous Time by Hodgson, James, Johansen, Adam M., Pollock, Murray

“…For rare events described in terms of Markov processes, truly unbiased estimation of the rare event probability generally requires the avoidance of numerical…”
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Product-form estimators: exploiting independence to scale up Monte Carlo by Kuntz, Juan, Crucinio, Francesca R., Johansen, Adam M.

“…We introduce a class of Monte Carlo estimators that aim to overcome the rapid growth of variance with dimension often observed for standard estimators by…”
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Rare Event Simulation for Stochastic Dynamics in Continuous Time by Angeli, Letizia, Grosskinsky, Stefan, Johansen, Adam M., Pizzoferrato, Andrea

“…Large deviations for additive path functionals of stochastic dynamics and related numerical approaches have attracted significant recent research interest. We…”
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ASYMPTOTIC GENEALOGIES OF INTERACTING PARTICLE SYSTEMS WITH AN APPLICATION TO SEQUENTIAL MONTE CARLO by Koskela, Jere, Jenkins, Paul A., Johansen, Adam M., Spanò, Dario

“…We study weighted particle systems in which new generations are resampled from current particles with probabilities proportional to their weights. This covers…”
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Bayesian model comparison with un-normalised likelihoods by Everitt, Richard G., Johansen, Adam M., Rowing, Ellen, Evdemon-Hogan, Melina

“…Models for which the likelihood function can be evaluated only up to a parameter-dependent unknown normalizing constant, such as Markov random field models,…”
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