English
Stochastic modeling for population dynamics: simulation and inference - Part 1
Multi angle
Auteurs : de Saporta, Benoîte (Auteur de la Conférence)
CIRM (Editeur )
60Jxx - Markov processes
90Cxx - Mathematical programming
92Bxx - Mathematical biology in general
CIRM (Editeur )
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Résumé :
The aim of this course is to present some examples of stochastic models suitable for population dynamics.
The first part will introduce a class of continuous time models called piecewise deterministic Markov processes (PDMPs). Their trajectories are deterministic with jumps at random times. They are especially suitable to model phenomena with different time scales: a fast time-sacla corresponding to the deterministic behaviour and a slow time-scale corresponding to the jumps. I'll present different biological systems that can be modelled by PDMPs, explain how they can be simulated.
The second part will focus on random models for cell division when the whole branching population is taken into account. I'll present two data sets from biological experiments trying to determine whether cell division is symmetric or not. I'll explain how statistic tools can help answer this question.
Keywords : Markov process; numeric probabilities; stochastic control; applications to biology
Codes MSC : The first part will introduce a class of continuous time models called piecewise deterministic Markov processes (PDMPs). Their trajectories are deterministic with jumps at random times. They are especially suitable to model phenomena with different time scales: a fast time-sacla corresponding to the deterministic behaviour and a slow time-scale corresponding to the jumps. I'll present different biological systems that can be modelled by PDMPs, explain how they can be simulated.
The second part will focus on random models for cell division when the whole branching population is taken into account. I'll present two data sets from biological experiments trying to determine whether cell division is symmetric or not. I'll explain how statistic tools can help answer this question.
Keywords : Markov process; numeric probabilities; stochastic control; applications to biology
60Jxx - Markov processes
90Cxx - Mathematical programming
92Bxx - Mathematical biology in general
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Informations sur la Vidéo
Réalisateur : Recanzone, LucaLangue : Anglais Date de publication : 21/02/2020 Date de captation : 03/02/2020 Sous collection : Research School arXiv category : Probability Domaine : Probability & Statistics Format : MP4 (.mp4) - HD Durée : 01:41:31 Audience : Researchers Download : https://videos.cirm-math.fr/2020-02-03_Saporta_Part1.mp4 
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Informations sur la Rencontre
Nom de la rencontre : Thematic Month Week 1: PDE and Probability for Biology / Mois thématique Semaine 1 : EDP et probabilité pour la biologieOrganisateurs de la rencontre : Chapuisat, Guillemette ; Cloez, Bertrand ; Henderson, Christopher ; Hubert, Florence ; Pudlo, Pierre ; Raoul, Gaël Dates : 03/02/2020 - 07/02/2020 Année de la rencontre : 2020 URL Congrès : https://conferences.cirm-math.fr/2301.html
Données de citation
DOI : 10.24350/CIRM.V.19604303Citer cette vidéo: de Saporta, Benoîte (2020). Stochastic modeling for population dynamics: simulation and inference - Part 1. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19604303 URI : http://dx.doi.org/10.24350/CIRM.V.19604303 |
Voir aussi
- [Multi angle] Stochastic modeling for population dynamics: simulation and inference - Part 3 / Auteur de la Conférence de Saporta, Benoîte.
- [Multi angle] Stochastic modeling for population dynamics: simulation and inference - Part 2 / Auteur de la Conférence de Saporta, Benoîte.
Bibliographie
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