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Stochastic modeling for population dynamics: simulation and inference - Part 1
Multi angle
Authors : de Saporta, Benoîte (Author of the conference)
CIRM (Publisher )
60Jxx - Markov processes
90Cxx - Mathematical programming
92Bxx - Mathematical biology in general
CIRM (Publisher )
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Abstract :
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
MSC Codes : 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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Information on the Video
Film maker : Recanzone, LucaLanguage : English Available date : 21/02/2020 Conference Date : 03/02/2020 Subseries : Research School arXiv category : Probability Mathematical Area(s) : Probability & Statistics Format : MP4 (.mp4) - HD Video Time : 01:41:31 Targeted Audience : Researchers Download : https://videos.cirm-math.fr/2020-02-03_Saporta_Part1.mp4 
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Information on the Event
Event Title : Thematic Month Week 1: PDE and Probability for Biology / Mois thématique Semaine 1 : EDP et probabilité pour la biologieEvent Organizers : Chapuisat, Guillemette ; Cloez, Bertrand ; Henderson, Christopher ; Hubert, Florence ; Pudlo, Pierre ; Raoul, Gaël Dates : 03/02/2020 - 07/02/2020 Event Year : 2020 Event URL : https://conferences.cirm-math.fr/2301.html
Citation Data
DOI : 10.24350/CIRM.V.19604303Cite this video as: 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 |
See Also
- [Multi angle] Stochastic modeling for population dynamics: simulation and inference - Part 3 / Author of the conference de Saporta, Benoîte.
- [Multi angle] Stochastic modeling for population dynamics: simulation and inference - Part 2 / Author of the conference de Saporta, Benoîte.
Bibliography
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