An Introduction to Continuous Time Stochastic Processes: by Vincenzo Capasso, David Bakstein

By Vincenzo Capasso, David Bakstein

"This publication is an advent to the speculation of continuous-time stochastic procedures. A stability of idea and purposes, the paintings gains concrete examples of modeling real-world difficulties from biology, drugs, finance, and coverage utilizing stochastic equipment. An creation to Continuous-Time Stochastic tactics may be of curiosity to a extensive viewers of scholars, natural and utilized mathematicians, and researchers or practitioners in mathematical finance, biomathematics, biotechnology, physics, and engineering. compatible as a textbook for graduate or complex undergraduate classes, the paintings can also be used for self-study or as a reference.

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Additional info for An Introduction to Continuous Time Stochastic Processes: Theory, Models, and Applications to Finance, Biology, and Medicine

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The subset T0 of R+ , as defined above, is called the separating set. 26. Let (Xt )t∈R+ be a separable process, having T0 and A as its separating and negligible sets, respectively. If ω ∈ / A, t0 ∈ R+ , and limt→t0 Xt (ω) for t ∈ T0 exists, then so does the limit limt→t0 Xt (ω) for t ∈ R+ , and they coincide. , Ash and Gardner (1975).

As well as n ∈ N, ∞ T = σ(An , An+1 , . ) n=1 be σ-algebras. Then T is the tail σ-algebra associated with the sequence (An )n∈N and its elements are called tail events. 96. The essential supremum ∞ ∞ lim sup An = n and essential infimum Ai n=1 i=n ∞ ∞ lim inf An = n Ai n=1 i=n are both tail events for the sequence (An )n∈N . ). ), thus for all but finitely many n ∈ N. 97. (Kolmogorov’s zero-one law). Let (An )n∈N ∈ F N be a sequence of independent events. Then for any A ∈ T : P (A) = 0 or P (A) = 1.

Give an example of two random variables that are uncorrelated but not independent. 6. If X has an absolutely continuous distribution with pdf f (x), its entropy is defined as H(X) = − f (x) ln f (x)dx, D where D = {x ∈ R|f (x) > 0}. 1. Show that the maximal value of entropy within the set of nonnegative random variables with a given expected value μ is attained by the exponential E(μ−1 ). 2. Show that the maximal value of entropy within the set of real random variables with fixed mean μ and variance σ 2 is attained by the Gaussian N (μ, σ 2 ).

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