Read e-book online An Introduction to Continuous-Time Stochastic Processes: PDF

By Vincenzo Capasso, David Bakstein

ISBN-10: 0817632344

ISBN-13: 9780817632342

ISBN-10: 0817644288

ISBN-13: 9780817644284

This concisely written publication is a rigorous and self-contained creation 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, commercial functions, finance, and coverage utilizing stochastic tools. No past wisdom of stochastic approaches is required.

Key themes lined include:

* Interacting debris and agent-based types: from polymers to ants

* inhabitants dynamics: from start and dying strategies to epidemics

* monetary marketplace types: the non-arbitrage precept

* Contingent declare valuation types: the risk-neutral valuation thought

* hazard research in assurance

An creation to Continuous-Time Stochastic Processes can be of curiosity to a large viewers of scholars, natural and utilized mathematicians, and researchers or practitioners in mathematical finance, biomathematics, biotechnology, and engineering. compatible as a textbook for graduate or complicated undergraduate classes, the paintings can also be used for self-study or as a reference. must haves contain wisdom of calculus and a few research; publicity to likelihood will be invaluable yet now not required because the useful basics of degree and integration are provided.

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Read or Download An Introduction to Continuous-Time Stochastic Processes: Theory, Models, and Applications to Finance, Biology, and Medicine PDF

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Extra resources for An Introduction to Continuous-Time Stochastic Processes: Theory, Models, and Applications to Finance, Biology, and Medicine

Sample text

Further, if we arbitrarily fix the value of P (F |X = x) at the points x ∈ E where PX is zero, then we can extend the mapping x ∈ EX → P (F |X = x) to the whole of E, so that P (·|X = x) : F → [0, 1] is again a probability measure on F, defined almost surely with respect to PX . 105. The family of functions (P (·|X = x))x∈E is called a regular version of the conditional probability with respect to X. 106. Let (P (·|X = x))x∈E be a regular version of the conditional probability with respect to X. s.

20. Give an example of random variables X, X1 , X2 , . . such that (Xn )n∈N converges to X • • • • in probability but not almost surely, in probability but not in mean, almost surely but not in mean and vice versa, in mean of order 1 but not in mean of order p = 2 (generally p > 1). 21. Let (Xn )n∈N be a sequence of independent and identically distributed random variables such that Xi ∼ B(p) for all i. Let Y be uniformly distributed n on [0, 1] and independent of Xi , for all i. If Sn = n1 k=1 (Xk − Y )2 , show that (Sn )n∈N converges almost surely and determine its limit.

16. Two processes are indistinguishable if P (Xt = Yt , ∀t ∈ R+ ) = 1. 17. It is obvious that two indistinguishable processes are modifications of each other. 18. Let (Xt )t∈T be a family of independent random variables defined on (Ω, F, P ) and valued in (E, B). ) We know that for all t ∈ T the probability Pt = Xt (P ) is defined on (E, B). Then r ∀S = {t1 , . . , tr } ∈ S : PS = Ptk , for some r ∈ N∗ , k=1 and the system (PS )S∈S is compatible with its finite products (E S , BS )S∈S . , B = Bt1 × · · · × Btr , and if S ⊂ S , where S, S ∈ S, then PS (B) = PS (Bt1 × · · · × Btr ) = Pt1 (Bt1 ) · · · · · Ptr (Btr ) = Pt1 (Bt1 ) · · · · · Ptr (Btr )Ptr+1 (E) · · · · · Ptr (E) −1 (B)).

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An Introduction to Continuous-Time Stochastic Processes: Theory, Models, and Applications to Finance, Biology, and Medicine by Vincenzo Capasso, David Bakstein


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