Time-dependent probability density functions and information geometry in stochastic logistic and Gompertz models

Lucille Marie Tenkes, Rainer Hollerbach, Eun Jin Kim

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

A probabilistic description is essential for understanding growth processes in non-stationary states. In this paper, we compute time-dependent probability density functions (PDFs) in order to investigate stochastic logistic and Gompertz models, which are two of the most popular growth models. We consider different types of short-correlated multiplicative and additive noise sources and compare the time-dependent PDFs in the two models, elucidating the effects of the additive and multiplicative noises on the form of PDFs. We demonstrate an interesting transition from a unimodal to a bimodal PDF as the multiplicative noise increases for a fixed value of the additive noise. A much weaker (leaky) attractor in the Gompertz model leads to a significant (singular) growth of the population of a very small size. We point out the limitation of using stationary PDFs, mean value and variance in understanding statistical properties of the growth in non-stationary states, highlighting the importance of time-dependent PDFs. We further compare these two models from the perspective of information change that occurs during the growth process. Specifically, we define an infinitesimal distance at any time by comparing two PDFs at times infinitesimally apart and sum these distances in time. The total distance along the trajectory quantifies the total number of different states that the system undergoes in time, and is called the information length. We show that the time-evolution of the two models become more similar when measured in units of the information length and point out the merit of using the information length in unifying and understanding the dynamic evolution of different growth processes.

Original languageEnglish
Article number123201
JournalJournal of Statistical Mechanics: Theory and Experiment
Volume2017
Issue number12
DOIs
Publication statusPublished - 6 Dec 2017
Externally publishedYes

Fingerprint

Information Geometry
logistics
probability density functions
Probability density function
Logistics
geometry
Multiplicative Noise
Growth Process
Additive Noise
Model
Bimodal
Growth Model
Logistic model
Gompertz model
Geometry
Mean Value
Statistical property
Attractor
Quantify
trajectories

Keywords

  • population dynamics
  • stochastic processes

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Time-dependent probability density functions and information geometry in stochastic logistic and Gompertz models. / Tenkes, Lucille Marie; Hollerbach, Rainer; Kim, Eun Jin.

In: Journal of Statistical Mechanics: Theory and Experiment, Vol. 2017, No. 12, 123201, 06.12.2017.

Research output: Contribution to journalArticle

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