Introduction to stochastic models in biology

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Introduction to stochastic models in biology. / Ditlevsen, Susanne; Samson, Adeline.

Stochastic Biomathematical Models: with Applications to Neuronal Modeling. red. / Mostafa Bachar; Jerry Batzel; Susanne Ditlevsen. Berlin : Springer, 2013. s. 3-34 (Lecture Notes in Mathematics; Nr. 2058).

Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › fagfællebedømt

Harvard

Ditlevsen, S & Samson, A 2013, Introduction to stochastic models in biology. i M Bachar, J Batzel & S Ditlevsen (red), Stochastic Biomathematical Models: with Applications to Neuronal Modeling. Springer, Berlin, Lecture Notes in Mathematics, nr. 2058, s. 3-34. https://doi.org/10.1007/978-3-642-32157-3_1

APA

Ditlevsen, S., & Samson, A. (2013). Introduction to stochastic models in biology. I M. Bachar, J. Batzel, & S. Ditlevsen (red.), Stochastic Biomathematical Models: with Applications to Neuronal Modeling (s. 3-34). Springer. Lecture Notes in Mathematics Nr. 2058 https://doi.org/10.1007/978-3-642-32157-3_1

Vancouver

Ditlevsen S, Samson A. Introduction to stochastic models in biology. I Bachar M, Batzel J, Ditlevsen S, red., Stochastic Biomathematical Models: with Applications to Neuronal Modeling. Berlin: Springer. 2013. s. 3-34. (Lecture Notes in Mathematics; Nr. 2058). https://doi.org/10.1007/978-3-642-32157-3_1

Author

Ditlevsen, Susanne ; Samson, Adeline. / Introduction to stochastic models in biology. Stochastic Biomathematical Models: with Applications to Neuronal Modeling. red. / Mostafa Bachar ; Jerry Batzel ; Susanne Ditlevsen. Berlin : Springer, 2013. s. 3-34 (Lecture Notes in Mathematics; Nr. 2058).

Bibtex

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title = "Introduction to stochastic models in biology",

abstract = "This chapter is concerned with continuous time processes, which are often modeled as a system of ordinary differential equations (ODEs). These models assume that the observed dynamics are driven exclusively by internal, deterministic mechanisms. However, real biological systems will always be exposed to influences that are not completely understood or not feasible to model explicitly. Ignoring these phenomena in the modeling may affect the analysis of the studied biological systems. Therefore there is an increasing need to extend the deterministic models to models that embrace more complex variations in the dynamics. A way of modeling these elements is by including stochastic influences or noise. A natural extension of a deterministic differential equations model is a system of stochastic differential equations (SDEs), where relevant parameters are modeled as suitable stochastic processes, or stochastic processes are added to the driving system equations. This approach assumes that the dynamics are partly driven by noise.",

author = "Susanne Ditlevsen and Adeline Samson",

year = "2013",

doi = "10.1007/978-3-642-32157-3_1",

language = "English",

isbn = "978-3-642-32156-6",

series = "Lecture Notes in Mathematics",

publisher = "Springer",

number = "2058",

pages = "3--34",

editor = "Mostafa Bachar and Jerry Batzel and Susanne Ditlevsen",

booktitle = "Stochastic Biomathematical Models",

address = "Switzerland",

}

RIS

TY - CHAP

T1 - Introduction to stochastic models in biology

AU - Ditlevsen, Susanne

AU - Samson, Adeline

PY - 2013

Y1 - 2013

N2 - This chapter is concerned with continuous time processes, which are often modeled as a system of ordinary differential equations (ODEs). These models assume that the observed dynamics are driven exclusively by internal, deterministic mechanisms. However, real biological systems will always be exposed to influences that are not completely understood or not feasible to model explicitly. Ignoring these phenomena in the modeling may affect the analysis of the studied biological systems. Therefore there is an increasing need to extend the deterministic models to models that embrace more complex variations in the dynamics. A way of modeling these elements is by including stochastic influences or noise. A natural extension of a deterministic differential equations model is a system of stochastic differential equations (SDEs), where relevant parameters are modeled as suitable stochastic processes, or stochastic processes are added to the driving system equations. This approach assumes that the dynamics are partly driven by noise.

AB - This chapter is concerned with continuous time processes, which are often modeled as a system of ordinary differential equations (ODEs). These models assume that the observed dynamics are driven exclusively by internal, deterministic mechanisms. However, real biological systems will always be exposed to influences that are not completely understood or not feasible to model explicitly. Ignoring these phenomena in the modeling may affect the analysis of the studied biological systems. Therefore there is an increasing need to extend the deterministic models to models that embrace more complex variations in the dynamics. A way of modeling these elements is by including stochastic influences or noise. A natural extension of a deterministic differential equations model is a system of stochastic differential equations (SDEs), where relevant parameters are modeled as suitable stochastic processes, or stochastic processes are added to the driving system equations. This approach assumes that the dynamics are partly driven by noise.

U2 - 10.1007/978-3-642-32157-3_1

DO - 10.1007/978-3-642-32157-3_1

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SN - 978-3-642-32156-6

T3 - Lecture Notes in Mathematics

SP - 3

EP - 34

BT - Stochastic Biomathematical Models

A2 - Bachar, Mostafa

A2 - Batzel, Jerry

A2 - Ditlevsen, Susanne

PB - Springer

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ID: 16890617