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===소스=== | ===소스=== | ||
<references /> | <references /> | ||
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+ | == 메타데이터 == | ||
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+ | ===위키데이터=== | ||
+ | * ID : [https://www.wikidata.org/wiki/Q1056809 Q1056809] | ||
+ | ===Spacy 패턴 목록=== | ||
+ | * [{'LOWER': 'wiener'}, {'LEMMA': 'process'}] |
2022년 8월 21일 (일) 19:00 기준 최신판
노트
말뭉치
- Given to the world by the American mathematician, philosopher, and founder of cybernetics Norbert Wiener, it is called, naturally enough, the Wiener process.[1]
- The next major concept to absorb on the path to understanding Wiener processes is that of the Gaussian process.[1]
- The Wiener process plays an important role in both pure and applied mathematics.[2]
- In pure mathematics, the Wiener process gave rise to the study of continuous time martingales.[2]
- The Wiener process has applications throughout the mathematical sciences.[2]
- A third characterisation is that the Wiener process has a spectral representation as a sine series whose coefficients are independent N(0, 1) random variables.[2]
- Wiener process (also called Brownian motion) is a stochastic process {Wt}t0+ indexed by nonnegative real numbers t with the following properties: (1) W0 = 0.[3]
- A Wiener process with initial value W0 = x is gotten by adding x to a standard Wiener process.[3]
- First, it explains, at least in part, why the Wiener process arises so commonly in nature.[3]
- It is advisable, when confronted with a problem about Wiener processes, to begin by reecting on how scaling might affect the answer.[3]
- The Wiener process plays an important role both in pure and applied mathematics.[4]
- The Wiener process can be constructed as the scaling limit of a random walk, or other discrete-time stochastic processes with stationary independent increments.[4]
- The Wiener measure is the probability law on the space of continuous functions g, with g(0) = 0, induced by the Wiener process.[4]
- Wiener process includes two parameters called drift rate \( \alpha \) that represent trend and degree of volatility \( \sigma \).[5]
- Figure above shows the comparison of Wiener process and random walk.[5]
- The Wiener process is using zero drift and the unit standard deviation to make it the same parameter with the random walk.[5]
- In general, Wiener process has much higher spread because of the factor of the square root of time.[5]
- This is why the Brownian motion is also called the Wiener process.[6]
- Solve Stochastic Dierential Equations with Stochastic Integral This chapter introduces the stochastic process (especially the Wiener process), Itos Lemma, and the stochastic intergral.[7]
소스
메타데이터
위키데이터
- ID : Q1056809
Spacy 패턴 목록
- [{'LOWER': 'wiener'}, {'LEMMA': 'process'}]