Probability Theory and Stochastic Processes with Applications
by Oliver Knill
Publisher: Overseas Press 2009
Number of pages: 382
This text covers material of a basic probability course, discrete stochastic processes including Martingale theory, continuous time stochastic processes like Brownian motion and stochastic differential equations, estimation theory, Vlasov dynamics, multi-dimensional moment problems, random maps, etc.
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by S. Watanabe - Tata Institute of Fundamental Research
The author's main purpose in these lectures was to study solutions of stochastic differential equations as Wiener functionals and apply to them some infinite dimensional functional analysis. This idea was due to P. Malliavin.
by M. Gubinelli, N. Perkowski - arXiv
The aim is to introduce the basic problems of non-linear PDEs with stochastic and irregular terms. We explain how it is possible to handle them using two main techniques: the notion of energy solutions and that of paracontrolled distributions.
by Anders Szepessy, et al. - KTH
The goal of this course is to give useful understanding for solving problems formulated by stochastic differential equations models in science, engineering and finance. Typically, these problems require numerical methods to obtain a solution.
by Daniel W. Stroock - Tata Institute of Fundamental Research
The author's purpose in these lectures was to provide some insight into the properties of solutions to stochastic differential equations. In order to read these notes, one need only know the basic Ito theory of stochastic integrals.