Summary of relevant studies that used the Laio and/or modified Laio
stochastic models of soil water dynamics Study Active root Vegetation type depth (cm) Laio et al.
There are already many useful models adapted in different environments, such as ray-tracing models [5-6], geometry-based deterministic models (GBDMs) [7], correlation-based
stochastic models (CBSMs) [8-9] and geometry-based
stochastic models (GBSMs) [10-11].
This PhD research aims to study stochastic games not only in this traditional sense but also by considering strategic games with utilities that are outputs of
stochastic models. Examples of this include Markov queuing processes.
Stochastic models incorporate discrete movements of individuals between epidemiological classes and not average rates at which individuals move between classes [13-15].
Building upon our previous
stochastic models [21-23] based on smartcard data, a stochastic modeling approach about passenger individual repositioning distance with posterior analysis is proposed in this paper, by extending our primary model about passenger longitudinal repositioning [24].
In order to check the relationship between the
stochastic model and its deterministic counterpart, we simulate the
stochastic models with different values of population sizes.
To model the random discharge of pollutants such as raw sewage into this designated portion of the Ganges, we suppose that these discharges occur in accordance with a stationary Poisson process (Tijms, A First Course in
Stochastic Models, 2003,) with rate or parameter a > 0.
Ewald, 2008: On modelling physical systems with
stochastic models: Diffusion versus Levy processes.
New
stochastic models for indoor wireless channels and their statistics are provided (eq.
In the next section we propose two
stochastic models: the first one is an endemic model in which each person infected from outside source contributes a random amount of infectivity to the force of infectivity (or infectivity function) which decreases at a constant rate over the time.
In addition,
stochastic models have been widely used in finance and insurance (such as [1-4]).
They limit their account
stochastic models in discrete time because the probabilistic machinery is simpler and they can discuss some of the key problems of pricing and hedging in financial derivatives right away.
