Stochastic control or stochastic optimal control is a sub field of control theory that deals with the existence of uncertainty either in observations or...

various applications such as stochastic control, mathematical finance, and nonlinear Feynman-Kac formulae. Backward stochastic differential equations were...

Optimal Control. Athena Scientific. Pham, Huyên (2009). "The Classical PDE Approach to Dynamic Programming". Continuous-time Stochastic Control and Optimization...

In probability theory and related fields, a stochastic (/stəˈkæstɪk/) or random process is a mathematical object usually defined as a sequence of random...

Stochastic (/stəˈkæstɪk/; from Ancient Greek στόχος (stókhos) 'aim, guess') is the property of being well-described by a random probability distribution...

one of the fundamental principles of stochastic control theory, which states that the problems of optimal control and state estimation can be decoupled...

Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e...

In control theory, robust control is an approach to controller design that explicitly deals with uncertainty. Robust control methods are designed to function...

small modeling errors. Stochastic control deals with control design with uncertainty in the model. In typical stochastic control problems, it is assumed...

Stationary process Stochastic calculus Itô calculus Malliavin calculus Semimartingale Stratonovich integral Stochastic control Stochastic differential equation...

separation principle is a special case of the separation principle of stochastic control which states that even when the process and output noise sources are...

solved by Davis and Norman in 1990. It is one of the few cases of stochastic singular control where the solution is known. For a graphical representation,...

Stochastic optimization (SO) are optimization methods that generate and use random variables. For stochastic optimization problems, the objective functions...

Markov decision process (MDP), also called a stochastic dynamic program or stochastic control problem, is a model for sequential decision making when outcomes...

approaches used in stochastic control theory. Regrettably the simple adaptation of the deterministic schemes for matching up to stochastic models such as...

controller designed to minimize a quadratic cost, is optimal for the stochastic control problem with output measurements. When process and observation noise...

Stochastic control Total quality management Barlow & Irony 1992 Bergman 2009 Zabell 1992 Deming, W. Edwards (1952). Lectures on statistical control of...

Sliding mode control SNOPT Stochastic control Trajectory optimization Ross, Isaac (2015). A primer on Pontryagin's principle in optimal control. San Francisco:...

stochastic dynamic programming is a technique for modelling and solving problems of decision making under uncertainty. Closely related to stochastic programming...

because it results in linear first-order conditions. In the context of stochastic control, the expected value of the quadratic form is used. The quadratic loss...

Intelligent control Optimal control Dynamic programming Robust control Stochastic control System dynamics, system analysis Takens' theorem Exponential dichotomy...

Cassandra, A.R. (1998). "Planning and acting in partially observable stochastic domains". Artificial Intelligence. 101 (1–2): 99–134. doi:10.1016/S0004-3702(98)00023-X...

Markov decision process Optional stopping theorem Prophet inequality Stochastic control Chow, Y.S.; Robbins, H.; Siegmund, D. (1971). Great Expectations:...

In the theory of stochastic processes, filtering describes the problem of determining the state of a system from an incomplete and potentially noisy set...

A stochastic differential equation (SDE) is a differential equation in which one or more of the terms is a stochastic process, resulting in a solution...

Stochastic resonance (SR) is a phenomenon in which a signal that is normally too weak to be detected by a sensor can be boosted by adding white noise to...

solutions using concepts from several control areas such as robust control, optimal stochastic control, model predictive control, fuzzy logic etc. A most critical...

populations. It lies at the intersection of game theory with stochastic analysis and control theory. The use of the term "mean field" is inspired by mean-field...

figure below, is a deceptively simple toy problem in decentralized stochastic control. It was formulated by Hans Witsenhausen in 1968. It is a counterexample...

In mathematics, a stochastic matrix is a square matrix used to describe the transitions of a Markov chain. Each of its entries is a nonnegative real number...