Introduction to Stochastic Control Theory
E285086
Introduction to Stochastic Control Theory is a foundational textbook that systematically develops the theory and methods for controlling dynamical systems under uncertainty using probabilistic and stochastic-process tools.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Introduction to Stochastic Control Theory canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T2646020 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Introduction to Stochastic Control Theory Context triple: [Karl J. Åström, notableWork, Introduction to Stochastic Control Theory]
-
A.
Foundations of a General Theory of Sequential Decision Functions
Foundations of a General Theory of Sequential Decision Functions is a seminal work in statistics that established the mathematical foundations of sequential analysis and optimal decision-making under uncertainty.
-
B.
Hamilton’s maximum principle
Hamilton’s maximum principle is a fundamental analytical tool in geometric analysis that extends the classical maximum principle to tensor-valued quantities, playing a key role in studying the behavior of solutions to the Ricci flow and related geometric evolution equations.
-
C.
Lyapunov stability theory
Lyapunov stability theory is a fundamental framework in dynamical systems and control theory that uses energy-like functions to assess the stability of equilibrium points without explicitly solving differential equations.
-
D.
Modern Probability Theory and Its Applications
"Modern Probability Theory and Its Applications" is a foundational textbook by Emanuel Parzen that systematically develops modern probability theory and demonstrates its use in a wide range of statistical and applied contexts.
-
E.
Feynman–Kac formula
The Feynman–Kac formula is a fundamental result connecting solutions of certain partial differential equations with expectations over stochastic processes, forming a bridge between quantum mechanics, probability theory, and mathematical finance.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Introduction to Stochastic Control Theory Target entity description: Introduction to Stochastic Control Theory is a foundational textbook that systematically develops the theory and methods for controlling dynamical systems under uncertainty using probabilistic and stochastic-process tools.
-
A.
Foundations of a General Theory of Sequential Decision Functions
Foundations of a General Theory of Sequential Decision Functions is a seminal work in statistics that established the mathematical foundations of sequential analysis and optimal decision-making under uncertainty.
-
B.
Hamilton’s maximum principle
Hamilton’s maximum principle is a fundamental analytical tool in geometric analysis that extends the classical maximum principle to tensor-valued quantities, playing a key role in studying the behavior of solutions to the Ricci flow and related geometric evolution equations.
-
C.
Lyapunov stability theory
Lyapunov stability theory is a fundamental framework in dynamical systems and control theory that uses energy-like functions to assess the stability of equilibrium points without explicitly solving differential equations.
-
D.
Modern Probability Theory and Its Applications
"Modern Probability Theory and Its Applications" is a foundational textbook by Emanuel Parzen that systematically develops modern probability theory and demonstrates its use in a wide range of statistical and applied contexts.
-
E.
Feynman–Kac formula
The Feynman–Kac formula is a fundamental result connecting solutions of certain partial differential equations with expectations over stochastic processes, forming a bridge between quantum mechanics, probability theory, and mathematical finance.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
book
ⓘ
textbook ⓘ |
| approach |
integration of estimation and control
ⓘ
rigorous mathematical treatment ⓘ systematic development of theory ⓘ |
| author | Karl J. Åström ⓘ |
| field |
applied mathematics
ⓘ
control theory ⓘ stochastic control ⓘ systems engineering ⓘ |
| focus |
design of optimal controllers
ⓘ
dynamical systems under uncertainty ⓘ probabilistic modeling of systems ⓘ use of stochastic-process tools in control ⓘ |
| genre |
engineering textbook
ⓘ
scientific literature ⓘ |
| hasApplicationArea |
aerospace engineering
ⓘ
automatic control ⓘ communications engineering ⓘ signal processing ⓘ |
| intendedAudience |
applied mathematicians
ⓘ
engineers ⓘ graduate students ⓘ researchers in control theory ⓘ |
| language | English ⓘ |
| subject |
Gaussian noise models
ⓘ
Kalman filtering ⓘ
linked to:
Kalman filter
Markov decision processes ⓘ
linked to:
Markov processes
certainty equivalence principle ⓘ continuous-time stochastic systems ⓘ discrete-time stochastic systems ⓘ dynamic programming ⓘ feedback control under uncertainty ⓘ filtering theory ⓘ innovation processes ⓘ linear quadratic Gaussian control ⓘ linear systems ⓘ optimal control ⓘ prediction and smoothing ⓘ quadratic cost criteria ⓘ separation principle ⓘ state estimation ⓘ stochastic processes ⓘ stochastic stability ⓘ |
| usesTool |
linear algebra
ⓘ
measure-theoretic probability ⓘ optimization theory ⓘ probability theory ⓘ stochastic processes ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Introduction to Stochastic Control Theory Description of subject: Introduction to Stochastic Control Theory is a foundational textbook that systematically develops the theory and methods for controlling dynamical systems under uncertainty using probabilistic and stochastic-process tools.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.