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Applied Stochastic Differential Equations

, 2019
The topic of this book is stochastic differential equations (SDEs). As their name suggests, they really are differential equations that produce a different “answer” or solution trajectory each time they are solved.
Simo Särkkä, Arno Solin
semanticscholar   +1 more source

Variational Inference for Stochastic Differential Equations

Annals of Physics, 2019
The statistical inference of the state variable and the drift function of stochastic differential equations (SDE) from sparsely sampled observations are discussed herein.
M. Opper
semanticscholar   +1 more source

Stochastic Differential Equations

2019
Abstract In this chapter we introduce stochastic differential equations (SDEs) and discuss existence and uniqueness questions. The geometric and linear equations are studied in some detail and their most important properties are derived. We then discuss the connection between SDEs and partial differential equations (PDEs).
V. Lakshmikantham, S.G. Deo
  +4 more sources

A Hitch-hiker’s Guide to Stochastic Differential Equations

, 2017
In this review, an overview of the recent history of stochastic differential equations (SDEs) in application to particle transport problems in space physics and astrophysics is given. The aim is to present a helpful working guide to the literature and at
R. D. Strauss, F. Effenberger
semanticscholar   +1 more source

Stochastic Differential Equations

1990
A stochastic (ordinary) differential equation (SDE) usually looks like this $$d{X^i}(t) = {\mu _i}(t,X(t))dt + \sum\limits_{j = 1}^d {{\sigma _{ij}}(t,X(t))d{B^j}(t),\quad 1 \leqslant i \leqslant n.} $$ (11.0.1)
Heinrich von Weizsäcker, Gerhard Winkler   +1 more
openaire   +1 more source

Stochastic differential equations

Stochastic differential equations serve as the foundation for many sections of applied sciences, such as mechanics, statistical physics, diffusion theory, cosmology, financial mathematics, economics, etc. The number of works devoted to various issues related to specific equations considered in individual areas of science listed above is very large.
openaire   +2 more sources

Stochastic Differential Equations

2014
Stochastic differential equations describe the time evolution of certain continuous n-dimensional Markov processes. In contrast with classical differential equations, in addition to the derivative of the function, there is a term that describes the random fluctuations that are coded as an Ito integral with respect to a Brownian motion. Depending on how
openaire   +1 more source

Stochastic Differential Equations

1991
In previous chapters stochastic differential equations have been mentioned several times in an informal manner. For instance, if M is a continuous local martingale, its exponential e(M) satisfies the equality $$\mathcal{E}{(M)_t} = 1 + \int_0^t {\mathcal{E}{{(M)}_s}} d{M_s};$$ this can be stated: e(M) is a solution to the stochastic differential
Daniel Revuz, Marc Yor
openaire   +1 more source

Stochastic Partial Differential Equations

Computer Vision, 2021
Annika Lang
semanticscholar   +1 more source

Mathematical Control Theory for Stochastic Partial Differential Equations

Probability Theory and Stochastic Modelling, 2021
Qi Lü, Xu Zhang
semanticscholar   +1 more source