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Date
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Technique Narrative
365
views
192
downloads
0
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1-030-RandomPerturbation-TechniqueNarrative
After a brief historical view of this problem, we will demonstrate the derivation of first order linear differential equations with random perturbations.
random perturbation
Brownian motion
Langevin equation
Riemann-Steiltjes integral
Wiener process
Ito's calculus
Modeling Scenario
443
views
174
downloads
0
comments
1-039-StochasticPopModels-ModelingScenario
We develop strategies for creating a population model using some simple probabilistic assumptions. These assumptions lead to a system of differential equations for the probability that a system is in state (or population size) n at time t.
Probability
population dynamics
Variance
Mean
stochastic
deterministic
Modeling Scenario
280
views
194
downloads
0
comments
1-108-PoissonProcess-ModelingScenario
In this project students learn to derive the probability density function (pdf) of the Poisson distribution and the cumulative distribution (cdf) of the waiting time. They will use them to solve problems in stochastic processes.
Probability
s
stochastic processes
Poisson process
waiting time
arrival
Modeling Scenario
289
views
213
downloads
0
comments
1-027-StochasticProcesses-ModelingScenario
We build the infinite set of first order differential equations for modeling a stochastic process, the so-called birth and death equations. We will only need to use integrating factor solution strategy or DSolve in Mathematica for success.
Variance
Mean
stochastic
random
operations research
industrial engineering
Poisson process
traffic
Modeling Scenario
390
views
147
downloads
0
comments
1-001s-StochasticMDeathImmigration-ModelingScenario
We develop a mathematical model of a death and immigration process using m&ms as a stochastic process with the help of probability generating functions (pgf). We start with 50 m&ms in a bag.
simulation
data
logistic
immigration
stochastic
death
fitting
Modeling Scenario
227
views
46
downloads
0
comments
1-141-MMGameRevisited-ModelingScenario
It is assumed that the probability of an M&M chocolate, when tossed, falling on the M side is 0.5 The goal is to find a probability distribution of the probability q which is Pr(randomly chosen M&M falling M up when tossed).
Probability
stochastic processes
Bayesian methods
probability distribution
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