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Stochastic Methods
In the mathematics of probability, a stochastic process is a random function. In practical applications, the domain over which the function is defined is a time interval (''time series'') or a region of space (''random field''). Familiar examples of time series include stock market and exchange rate fluctuations, signals such as speech, audio and video; medical data such as a patient's EKG, EEG, blood pressure or temperature; and random movement such as Brownian motion or random walks. Examples of random fields include static images, random topographies (landscapes), or composition variations of an inhomogeneous material. Stochastic processes topics :''This list is currently incomplete.'' See also :Stochastic processes * Basic affine jump diffusion * Bernoulli process: discrete-time processes with two possible states. ** Bernoulli schemes: discrete-time processes with ''N'' possible states; every stationary process in ''N'' outcomes is a Bernoulli scheme, and vice versa. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mathematics
Mathematics is an area of knowledge that includes the topics of numbers, formulas and related structures, shapes and the spaces in which they are contained, and quantities and their changes. These topics are represented in modern mathematics with the major subdisciplines of number theory, algebra, geometry, and analysis, respectively. There is no general consensus among mathematicians about a common definition for their academic discipline. Most mathematical activity involves the discovery of properties of abstract objects and the use of pure reason to prove them. These objects consist of either abstractions from nature orin modern mathematicsentities that are stipulated to have certain properties, called axioms. A ''proof'' consists of a succession of applications of deductive rules to already established results. These results include previously proved theorems, axioms, andin case of abstraction from naturesome basic properties that are considered true starting points of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gaussian Process
In probability theory and statistics, a Gaussian process is a stochastic process (a collection of random variables indexed by time or space), such that every finite collection of those random variables has a multivariate normal distribution, i.e. every finite linear combination of them is normally distributed. The distribution of a Gaussian process is the joint distribution of all those (infinitely many) random variables, and as such, it is a distribution over functions with a continuous domain, e.g. time or space. The concept of Gaussian processes is named after Carl Friedrich Gauss because it is based on the notion of the Gaussian distribution (normal distribution). Gaussian processes can be seen as an infinite-dimensional generalization of multivariate normal distributions. Gaussian processes are useful in statistical modelling, benefiting from properties inherited from the normal distribution. For example, if a random process is modelled as a Gaussian process, the distribution ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gamma Process
In mathematics and probability theory, a gamma process, also known as (Moran-)Gamma subordinator, is a random process with independent gamma distributed increments. Often written as \Gamma(t;\gamma,\lambda), it is a pure-jump increasing Lévy process with intensity measure \nu(x)=\gamma x^ \exp(-\lambda x), for positive x. Thus jumps whose size lies in the interval ,x+dx) occur as a Poisson process with intensity \nu(x)\,dx. The parameter \gamma controls the rate of jump arrivals and the scaling parameter \lambda inversely controls the jump size. It is assumed that the process starts from a value 0 at ''t'' = 0. The gamma process is sometimes also parameterised in terms of the mean (\mu) and variance (v) of the increase per unit time, which is equivalent to \gamma = \mu^2/v and \lambda = \mu/v. Properties Since we use the Gamma function in these properties, we may write the process at time t as X_t\equiv\Gamma(t;\gamma, \lambda) to eliminate ambiguity. Some ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Galton–Watson Process
The Galton–Watson process is a branching stochastic process arising from Francis Galton's statistical investigation of the extinction of family names. The process models family names as patrilineal (passed from father to son), while offspring are randomly either male or female, and names become extinct if the family name line dies out (holders of the family name die without male descendants). This is an accurate description of Y chromosome transmission in genetics, and the model is thus useful for understanding human Y-chromosome DNA haplogroups. Likewise, since mitochondria are inherited only on the maternal line, the same mathematical formulation describes transmission of mitochondria. The formula is of limited usefulness in understanding actual family name distributions, since in practice family names change for many other reasons, and dying out of name line is only one factor. History There was concern amongst the Victorians that aristocratic surnames were becoming e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
First Passage Time
Events are often triggered when a stochastic or random process first encounters a threshold. The threshold can be a barrier, boundary or specified state of a system. The amount of time required for a stochastic process, starting from some initial state, to encounter a threshold for the first time is referred to variously as a first hitting time. In statistics, first-hitting-time models are a sub-class of survival models. The first hitting time, also called first passage time, of the barrier set B with respect to an instance of a stochastic process is the time until the stochastic process first enters B. More colloquially, a first passage time in a stochastic system, is the time taken for a state variable to reach a certain value. Understanding this metric allows one to further understand the physical system under observation, and as such has been the topic of research in very diverse fields, from economics to ecology. The idea that a first hitting time of a stochastic process ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Finite-dimensional Distribution
In mathematics, finite-dimensional distributions are a tool in the study of measures and stochastic processes. A lot of information can be gained by studying the "projection" of a measure (or process) onto a finite-dimensional vector space (or finite collection of times). Finite-dimensional distributions of a measure Let (X, \mathcal, \mu) be a measure space. The finite-dimensional distributions of \mu are the pushforward measures f_ (\mu), where f : X \to \mathbb^, k \in \mathbb, is any measurable function. Finite-dimensional distributions of a stochastic process Let (\Omega, \mathcal, \mathbb) be a probability space and let X : I \times \Omega \to \mathbb be a stochastic process. The finite-dimensional distributions of X are the push forward measures \mathbb_^ on the product space \mathbb^ for k \in \mathbb defined by :\mathbb_^ (S) := \mathbb \left\. Very often, this condition is stated in terms of measurable rectangles: :\mathbb_^ (A_ \times \cdots \times A_) := \mathbb \ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dirichlet Process
In probability theory, Dirichlet processes (after the distribution associated with Peter Gustav Lejeune Dirichlet) are a family of stochastic processes whose realizations are probability distributions. In other words, a Dirichlet process is a probability distribution whose range is itself a set of probability distributions. It is often used in Bayesian inference to describe the prior knowledge about the distribution of random variables—how likely it is that the random variables are distributed according to one or another particular distribution. As an example, a bag of 100 real-world dice is a ''random probability mass function (random pmf)'' - to sample this random pmf you put your hand in the bag and draw out a die, that is, you draw a pmf. A bag of dice manufactured using a crude process 100 years ago will likely have probabilities that deviate wildly from the uniform pmf, whereas a bag of state-of-the-art dice used by Las Vegas casinos may have barely perceptible imperfe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cox Process
In probability theory, a Cox process, also known as a doubly stochastic Poisson process is a point process which is a generalization of a Poisson process where the intensity that varies across the underlying mathematical space (often space or time) is itself a stochastic process. The process is named after the statistician David Cox, who first published the model in 1955. Cox processes are used to generate simulations of spike trains (the sequence of action potentials generated by a neuron), and also in financial mathematics where they produce a "useful framework for modeling prices of financial instruments in which credit risk is a significant factor." Definition Let \xi be a random measure. A random measure \eta is called a Cox process directed by \xi , if \mathcal L(\eta \mid \xi=\mu) is a Poisson process with intensity measure \mu . Here, \mathcal L(\eta \mid \xi=\mu) is the conditional distribution of \eta , given \ . Laplace transform If \eta is a Cox pr ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Continuous Stochastic Process
In probability theory, a continuous stochastic process is a type of stochastic process that may be said to be "continuous" as a function of its "time" or index parameter. Continuity is a nice property for (the sample paths of) a process to have, since it implies that they are well-behaved in some sense, and, therefore, much easier to analyze. It is implicit here that the index of the stochastic process is a continuous variable. Some authorsDodge, Y. (2006) ''The Oxford Dictionary of Statistical Terms'', OUP. (Entry for "continuous process") define a "continuous (stochastic) process" as only requiring that the index variable be continuous, without continuity of sample paths: in some terminology, this would be a continuous-time stochastic process, in parallel to a "discrete-time process". Given the possible confusion, caution is needed. Definitions Let (Ω, Σ, P) be a probability space, let ''T'' be some interval of time, and let ''X'' : ''T'' ×&nb ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
CIR Process
CIR or Cir may refer to: Locations * Cairo Regional Airport, FAA/IATA code, CIR * CIR, station code for the Caledonian Road & Barnsbury railway station in the UK * Christmas Island Resort, a casino/resort in the northeastern Indian Oceans Organizations * Center for Individual Rights, a US non-profit public interest law firm * Center for Investigative Reporting, a nonprofit journalism organization * Christian Initiative Romero, a German non-profit organization supporting industrial law and human rights in Central America * Cosmetic Ingredient Review, a consumer safety group Politics and government * Commissioner of Internal Revenue, a US Treasury position * Committee on International Relations, renamed the United States House Committee on Foreign Affairs in 2007 * Comprehensive Immigration Reform Act of 2006, a US Senate bill * Convention of Republican Institutions, a defunct French political party * Citizen initiated Referendum, a type of referendum initiated by the signatur ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Chinese Restaurant Process
In probability theory, the Chinese restaurant process is a discrete-time stochastic process, analogous to seating customers at tables in a restaurant. Imagine a restaurant with an infinite number of circular tables, each with infinite capacity. Customer 1 sits at the first table. The next customer either sits at the same table as customer 1, or the next table. This continues, with each customer choosing to either sit at an occupied table with a probability proportional to the number of customers already there (i.e., they are more likely to sit at a table with many customers than few), or an unoccupied table. At time ''n'', the ''n'' customers have been partitioned among ''m'' ≤ ''n'' tables (or blocks of the partition). The results of this process are exchangeable, meaning the order in which the customers sit does not affect the probability of the final distribution. This property greatly simplifies a number of problems in population genetics, linguistic analysis, a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
