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

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

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

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

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

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...

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

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

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

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

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

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

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

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

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...

Reliability engineering Six sigma Stochastic control Total quality management Dutra, Noah; John, Demis. "Process Group - Process Control Data - UCSB Nanofab Wiki"...

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

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...

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

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

contributions to the theory and applications of stochastic processes, in particular to martingales, stochastic control and nonlinear filtering. Liptser was born...

London. He made fundamental contributions to the theory of stochastic processes, stochastic control and mathematical finance. After completing his BA degree...

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

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 control theory and mathematical finance. Her contributions focused on the mathematical theory of stochastic control, backward stochastic differential...

decentralized information systems and stochastic control. Demosthenis Teneketzis’ research is on Stochastic Control, Decentralized Decision-Making with...

mathematician known for his contributions to the theory of stochastic processes, queueing theory and control theory, as well as the design of telecommunications...

theory, the Mabinogion sheep problem or Mabinogian urn is a problem in stochastic control introduced by David Williams (1991, 15.3), who named it after a herd...

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...

control theorist and professor at the University of Toronto. He focused on multi-variable geometric control theory, stochastic control and stochastic...