Bayesian parameter estimation for the Wnt pathway: an infinite mixture models approach

Abstract : Likelihood-free methods, like Approximate Bayesian Computation (ABC), have been extensively used in model-based statistical inference with intractable likelihood functions. When combined with Sequential Monte Carlo (SMC) algorithms they constitute a powerful approach for parameter estimation and model selection of mathematical models of complex biological systems. A crucial step in the ABC–SMC algorithms, significantly affecting their performance, is the propagation of a set of parameter vectors through a sequence of intermediate distributions using Markov kernels.
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https://hal-centralesupelec.archives-ouvertes.fr/hal-01525063
Contributeur : Paul-Henry Cournède <>
Soumis le : vendredi 19 mai 2017 - 13:12:37
Dernière modification le : jeudi 5 avril 2018 - 12:30:26

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Konstantinos Koutroumpas, Paolo Ballarini, Irene Votsi, Paul-Henry Cournède. Bayesian parameter estimation for the Wnt pathway: an infinite mixture models approach. Bioinformatics, Oxford University Press (OUP), 2016, 32 (17), pp.781-789. 〈https://academic.oup.com/bioinformatics/article/32/17/i781/2450800/Bayesian-parameter-estimation-for-the-Wnt-pathway〉. 〈10.1093/bioinformatics/btw471 〉. 〈hal-01525063〉

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