# stochastic process meaning

This mathematical space can be defined using integers, real lines, It has been remarked that a notable exception was the St Petersburg School in Russia, where mathematicians led by Chebyshev studied probability theory. 1 The Deﬁnition of a Stochastic Process Suppose that (Ω,F,P) is a probability space, and that X : Ω → R is a random variable. ) ∈ Ω T {\displaystyle t_{i}\subset T} Ω p [139], The mathematical space [241][246], After Cardano, Jakob Bernoulli[e] wrote Ars Conjectandi, which is considered a significant event in the history of probability theory. {\displaystyle T} S 1 {\displaystyle T} and {\displaystyle t\in T} Ω X 1 {\displaystyle t\in T} that map from the set ≤ G ) 515. Ω {\displaystyle \{X(t,\omega ):t\in T\}} P {\displaystyle X} , which gives the interpretation of time. , such that for every open set X {\displaystyle n} [23][26], The term random function is also used to refer to a stochastic or random process,[27][28] because a stochastic process can also be interpreted as a random element in a function space. ω But the space also has functions with discontinuities, which means that the sample functions of stochastic processes with jumps, such as the Poisson process (on the real line), are also members of this space. adj. ∈ n {\displaystyle T} a statistical process involving a number of random variables depending on a variable parameter (which is usually time). [29][70], The set R → S The two types of stochastic processes are respectively referred to as discrete-time and continuous-time stochastic processes. 자세히 알아보기. [204][205], The concept of the Markov property was originally for stochastic processes in continuous and discrete time, but the property has been adapted for other index sets such as [300][304], Andrei Kolmogorov developed in a 1931 paper a large part of the early theory of continuous-time Markov processes. . Y [237][238] Other stochastic processes such as renewal and counting processes are studied in the theory of point processes. ] ) process. [39] The study of stochastic processes uses mathematical knowledge and techniques from probability, calculus, linear algebra, set theory, and topology[40][41][42] as well as branches of mathematical analysis such as real analysis, measure theory, Fourier analysis, and functional analysis. , ∘ X ∈ We're doing our best to make sure our content is useful, accurate and safe.If by any chance you spot an inappropriate comment while navigating through our website please use this form to let us know, and we'll take care of it shortly. P [311][317], Another problem is that functionals of continuous-time process that rely upon an uncountable number of points of the index set may not be measurable, so the probabilities of certain events may not be well-defined. Many stochastic processes can be represented by time series. {\displaystyle \left(Y(t_{1}),\ldots ,Y(t_{n})\right)} -dimensional Euclidean space, where an element , Martingales are usually defined to be real-valued,[209][210][156] but they can also be complex-valued[211] or even more general. Y [53][156] The intuition behind a filtration such that I p {\displaystyle X(t)} , {\displaystyle S} had the meaning of time, so ∞ A good way to think about it, is that a stochastic process is the opposite of a deterministic process. differ from each other at most on a subset of [24][133] But it has been remarked that the Poisson process does not receive as much attention as it should, partly due to it often being considered just on the real line, and not on other mathematical spaces. t [126] Serving as a fundamental process in queueing theory, the Poisson process is an important process for mathematical models, where it finds applications for models of events randomly occurring in certain time windows. Although the random values of a stochastic process at different times may be independent random variables, in most commonly considered situations they exhibit complicated statistical correlations. is interpreted as time, a sample path of the stochastic process ) {\displaystyle (\Omega ,{\mathcal {F}},P)} [24][296] There are a number of claims for early uses or discoveries of the Poisson , In other words, a stochastic process {\displaystyle G\subset T} Recall that this means that Ω is a space, F is a σ-algebra of subsets of Ω, P is a countably additive, non-negative measure on (Ω,F) with total mass P(Ω) = … {\displaystyle \Omega _{0}} ∈ ( Given a stochastic process , the natural filtration for (or induced by) this process is the filtration where is generated by all values of up to time s = t. I.e. 0 The Mean Function of a stochastic process First, we consider the mean function. at X X , , 1.2 Stochastic Processes Deﬁnition: A stochastic process is a family of random variables, {X(t) : t ∈ T}, where t usually denotes time. t [5][31] If the random variables are indexed by the Cartesian plane or some higher-dimensional Euclidean space, then the collection of random variables is usually called a random field instead. T {\displaystyle 1-p} . [91][280] For example, the problem known as the Gambler's ruin is based on a simple random walk,[196][281] and is an example of a random walk with absorbing barriers. {\displaystyle Y} , the finite-dimensional distributions of a stochastic process X [192][193], The Brownian motion process and the Poisson process (in one dimension) are both examples of Markov processes[194] in continuous time, while random walks on the integers and the gambler's ruin problem are examples of Markov processes in discrete time. ( ( T {\displaystyle T=[0,\infty )} Y 2 ] {\displaystyle S} [231][232] Some authors regard a point process and stochastic process as two different objects such that a point process is a random object that arises from or is associated with a stochastic process,[233][234] though it has been remarked that the difference between point processes and stochastic processes is not clear. {\displaystyle \left\{X_{t}\right\}} How to use stochastic in a sentence. The publication of this book is now widely considered to be the birth of modern probability theory, when the theories of probability and stochastic processes became parts of mathematics. Einstein derived a differential equation, known as a diffusion equation, for describing the probability of finding a particle in a certain region of space. [266] Doob also chiefly developed the theory of martingales, with later substantial contributions by Paul-André Meyer. , which must be measurable with respect to some {\displaystyle n} Strongly stationary stochastic processes The meaning of the strongly stationarity is that the distribution of a number of random variables of the stochastic process is the same as we shift them along the time index axis. -valued functions of R ⊂ [307] The differential equations are now called the Kolmogorov equations[308] or the Kolmogorov–Chapman equations. {\displaystyle {\mathcal {F}}_{t}} t X Meaning of stochastic processes for the defined word. t 에서 한국어 내부, 우리는 어떻게 설명 할stochastic processes영어 단어 그것은? ) Later in the 1960s and 1970s fundamental work was done by Alexander Wentzell in the Soviet Union and Monroe D. Donsker and Srinivasa Varadhan in the United States of America,[273] which would later result in Varadhan winning the 2007 Abel Prize. is a stationary stochastic process, then for any 2 [209][215], Martingales mathematically formalize the idea of a fair game,[216] and they were originally developed to show that it is not possible to win a fair game. Σ is zero for all times.[179]:p. [169][319], Another approach is possible, originally developed by Anatoliy Skorokhod and Andrei Kolmogorov,[320] for a continuous-time stochastic process with any metric space as its state space. n , which is a real number, then the resulting stochastic process is said to have drift , although This type of stochastic process can be used to describe a physical system that is in steady state, but still experiences random fluctuations. or simply as ) {\displaystyle h} Y In the context of point processes, the term "state space" can mean the space on which the point process is defined such as the real line. 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