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数理人口学・数理生物学セミナー
過去の記録 ～04/19｜次回の予定｜今後の予定 04/20～
2015年11月18日(水)
14:55-16:40 数理科学研究科棟(駒場) 128号室
高須夫悟 氏 (奈良女子大学理学部情報科学科)
Spatial population dynamics as a point pattern dynamics (JAPANESE)
http://www.ics.nara-wu.ac.jp/jp/staff/takasu.html
高須夫悟 氏 (奈良女子大学理学部情報科学科)
Spatial population dynamics as a point pattern dynamics (JAPANESE)
[ 講演概要 ]
Spatial population dynamics has been conventionally described as
dynamical system where population size (or population density) changes
with time over space as a continuous "real-valued" variable; these are
often given as partial differential equations as reaction-diffusion
models. In this approach, we implicitly assume infinitely large
population thereby population size changes smoothly and
deterministically. In reality, however, a population is a collection of
a certain number of individuals each of which gives birth or dies with
some stochasticity in a space and the population size as the number of
individuals is "integer-valued". In this talk, I introduce an approach
to reconstruct conventional spatial population dynamics in terms of
point pattern dynamics as a stochastic process. I discuss how to
mathematically describe such spatial stochastic processes using the
moments of increasing order of dimension; densities of points, pairs,
and triplets, etc. are described by integro-differential equations.
Quantification of a point pattern is the key issue here. As examples, I
introduce spatial epidemic SIS and SIR models as point pattern dynamics;
each individual has a certain "mark" depending on its health status; a
snapshot of individuals’ distribution over space is represented by a
marked point pattern and this marked point pattern dynamically changes
with time.
[ 参考URL ]Spatial population dynamics has been conventionally described as
dynamical system where population size (or population density) changes
with time over space as a continuous "real-valued" variable; these are
often given as partial differential equations as reaction-diffusion
models. In this approach, we implicitly assume infinitely large
population thereby population size changes smoothly and
deterministically. In reality, however, a population is a collection of
a certain number of individuals each of which gives birth or dies with
some stochasticity in a space and the population size as the number of
individuals is "integer-valued". In this talk, I introduce an approach
to reconstruct conventional spatial population dynamics in terms of
point pattern dynamics as a stochastic process. I discuss how to
mathematically describe such spatial stochastic processes using the
moments of increasing order of dimension; densities of points, pairs,
and triplets, etc. are described by integro-differential equations.
Quantification of a point pattern is the key issue here. As examples, I
introduce spatial epidemic SIS and SIR models as point pattern dynamics;
each individual has a certain "mark" depending on its health status; a
snapshot of individuals’ distribution over space is represented by a
marked point pattern and this marked point pattern dynamically changes
with time.
http://www.ics.nara-wu.ac.jp/jp/staff/takasu.html
