An Envelope Method for Solving Continuous-Time Stochastic Models with Occasionally Binding Constraints

2021 ◽  
Author(s):  
Neil Ware White
Keyword(s):  
Stochastic Models ◽  
Continuous Time ◽  
Envelope Method ◽  
Binding Constraints ◽  
Occasionally Binding Constraints

Solving Dynamic Stochastic Models with Multiple Occasionally Binding Constraints

2021 ◽  
pp. 105636
Author(s):  
V. Ernesto Guerra ◽  
H. Eugenio Bobenrieth ◽  
H. Juan Bobenrieth ◽  
Brian D. Wright
Keyword(s):  
Stochastic Models ◽  
Dynamic Stochastic ◽  
Binding Constraints ◽  
Occasionally Binding Constraints

STOCHASTIC MODELING OF PHYTOPLANKTON–ZOOPLANKTON INTERACTIONS WITH TOXIN PRODUCING PHYTOPLANKTON

2018 ◽  
Vol 26 (01) ◽  
pp. 87-106 ◽  
Author(s):  
T. MIHIRI M. DE SILVA ◽  
SOPHIA R.-J. JANG
Keyword(s):  
Markov Chain ◽  
Stochastic Modeling ◽  
Stochastic Models ◽  
Continuous Time ◽  
Toxin Production ◽  
Mutual Interference ◽  
Deterministic System ◽  
Continuous Time Markov Chain ◽  
Deterministic Systems ◽  
Population Interactions

We construct models of continuous-time Markov chain (CTMC) and Itô stochastic differential equations of population interactions based on a deterministic system of two phytoplankton and one zooplankton populations. The mechanisms of mutual interference among the predator zooplankton and the avoidance of toxin-producing phytoplankton (TPP) by zooplankton are incorporated. Sudden population extinctions occur in the stochastic models that cannot be captured in the deterministic systems. In addition, the effect of periodic toxin production by TPP is lessened when the birth and death of the populations are modeled randomly.

Markov chains and generalized continued fractions

1992 ◽  
Vol 29 (04) ◽  
pp. 838-849 ◽  
Author(s):  
Thomas Hanschke
Keyword(s):  
Markov Chains ◽  
Discrete Time ◽  
Wide Class ◽  
Stochastic Models ◽  
Continuous Time ◽  
Continued Fractions ◽  
Invariant Measures ◽  
Continuous Time Markov Chains ◽  
Generalized Continued Fractions

This paper deals with a class of discrete-time Markov chains for which the invariant measures can be expressed in terms of generalized continued fractions. The representation covers a wide class of stochastic models and is well suited for numerical applications. The results obtained can easily be extended to continuous-time Markov chains.

Continuous-time stochastic models of a multigrade population

1978 ◽  
Vol 15 (1) ◽  
pp. 26-37 ◽  
Author(s):  
Sally I. McClean
Keyword(s):  
Markov Model ◽  
Exponential Distribution ◽  
Stochastic Models ◽  
Continuous Time ◽  
Time Dependent ◽  
Poisson Arrival ◽  
Mixed Exponential Distribution ◽  
Previous Assumption

The continuous-time Markov model of a multigrade organization is extended in several ways. Firstly the internal transitions and the leaving process are generalized to a semi-Markov formulation which allows for the inclusion of well-authenticated leaving distributions such as the mixed exponential distribution. The previous assumption of Poisson recruitment is then generalized to allow for a time-dependent Poisson arrival distribution in which the instantaneous probability of an arrival is a mixture of exponential terms. Finally we extend the capital-related manpower model to describe a multigrade organization.

An IV-Scheme for Estimating Continuous-Time Stochastic Models from Discrete-Time Data

1994 ◽  
Vol 27 (8) ◽  
pp. 1561-1566 ◽  
Author(s):  
S. Bigi ◽  
T. Söderström ◽  
B. Carlsson
Keyword(s):  
Discrete Time ◽  
Stochastic Models ◽  
Continuous Time ◽  
Time Data
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