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Reversible jump Markov chain Monte Carlo and multi-model samplers
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Reversible jump Markov chain Monte Carlo and multi-model samplers
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To appear in the second edition of the MCMC handbook, S. P. Brooks, A. Gelman, G. Jones and X.-L. Meng (eds), Chapman & Hall.
Forward citations
Cited by 2 Pith papers
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A story about a tipsy kangaroo: Reversible jump MCMC for model selection in the analysis of gravitational-wave signals from the coalescence of compact objects
A single RJMCMC run can rank BBH, NSBH, and BNS waveform models and deliver the favored model's parameter posteriors, validated on injections and two real GW events.
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Neural posterior estimation of Galactic Binary signals for the LISA mission
Conditional normalizing flows perform likelihood-free parameter estimation for single and overlapping LISA galactic binaries, generating thousands of posterior samples per second after training on simulations.
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