Probability theory and stochastic processes Stochastic processes: general theory Concept
Stochastic process
Builds on Random variableProbability space
Summary
A stochastic process is a family of random variables on one probability space , all with values in the same measurable space . The set is the index set, often a set of times such as or . The set is the state space. For each outcome , the function is a sample path of the process.
Remarks
The process is in discrete time if is countable, such as , and in continuous time if is an interval. The finite-dimensional distributions are the laws of the vectors for finite sets . The Kolmogorov extension theorem builds a process from a consistent family of such laws. Important examples are the Bernoulli process, random walks, Markov chains, the Poisson process and the Wiener process.
Reading path
To understand this page from the ground up, read these 39 pages in order. The foundations come first.
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- no evidence level
- Construction of the reals from Cauchy sequences concept
- Element definition
- Integers object
- Ordered field definition
- Proposition concept
- Random experiment concept
- Rational numbers object
- Real numbers object
- Set definition
- Ring of integers statement
- Disjunction definition
- Implication definition
- Predicate definition
- Ordered pair definition
- Union definition
- Sample space definition
- The rationals form a field statement
- Existential quantifier definition
- Universal quantifier definition
- Cartesian product definition
- Subset definition
- The rationals form an ordered field statement
- Uniqueness quantifier definition
- Binary relation definition
- Universal set definition
- Order on the reals definition
- Power set definition
- Complement definition
- Function definition
- Interval definition
- Preimage of a set definition
- Sigma-algebra definition
- Event definition
- Borel sigma-algebra definition
- Probability axioms concept
- Probability measure definition
- Probability space definition
- Random variable definition
- Stochastic process concept
Where this leads
67 pages build on this one.
- Chapman–Kolmogorov equation
- Stationary distribution
- Bernoulli process
- Markov chain
- Poisson point process
- Random walk
- Martingale
- Transition matrix
- Galton–Watson process
- Markov property
- Gambler's ruin
- Absorbing Markov chain
- Doob's upcrossing inequality
- Functional data analysis
- Stationary process
- Boolean model
- Stochastic partial differential equation
- Doob's martingale inequality
- Brownian bridge
- Wiener process
- Karhunen–Loève theorem
- Variogram
- Ornstein–Uhlenbeck process
- Adapted process
- Lévy's characterization of Brownian motion
- Metropolis–Hastings algorithm
- Continuous-time Markov chain
- Kriging
- Convergence to equilibrium of ergodic Markov chains
- Detailed balance
- Coupling from the past
- Hidden Markov model
- Finite-dimensional distributions
- Lévy process
- Wald's equation
- Random ordinary differential equation
- Time series
- Optional stopping theorem
- Submartingale
- Stochastic difference equation
- Recurrence and transience of states
- Birth–death process
- Tanaka's formula
- Geometric Brownian motion
- Hunt process
- Markov chain mixing time
- Reflection principle
- Self-similar process
- Gibbs sampling
- Supermartingale
- Markov chain Monte Carlo
- Doob's martingale convergence theorems
- Random integral equation
- Semimartingale
- Stopped process
- Fractional Brownian motion
- Gaussian process
- Burkholder–Davis–Gundy inequality
- Doob–Meyer decomposition theorem
- Kolmogorov continuity theorem
- Empirical process
- Skorokhod's embedding theorem
- Doob decomposition theorem
- Girsanov theorem
- Quadratic variation
- Feller process
- Local martingale
Further reading
- Web https://en.wikipedia.org/wiki/Stochastic_process, Stochastic process
About this page
| Tag | 0QH5ENKZM1 |
|---|---|
| URI | m2://0QH5ENKZM1 |
| Aliases | random-process |
| Kind | concept |
| Writing | W1 stated |
| MSC | 60G05 |
| Uses | Random variable, Probability space |
| Same as |
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What this page needs for W2
- edge defines in: at least one definition (0 of 1)
- edge instance_of in: at least two examples (0 of 2)
- edge not_instance_of in: at least one non-example (0 of 1)
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What links here
Used by
- Bernoulli process
- Markov chain
- Poisson point process
- Random walk
- Martingale
- Galton–Watson process
- Markov property
- Functional data analysis
- Stationary process
- Stochastic partial differential equation
- Wiener process
- Karhunen–Loève theorem
- Variogram
- Adapted process
- Continuous-time Markov chain
- Kriging
- Finite-dimensional distributions
- Lévy process
- Random ordinary differential equation
- Time series
- Stochastic difference equation
- Hunt process
- Self-similar process
- Random integral equation
- Stopped process
- Gaussian process
- Kolmogorov continuity theorem
- Empirical process
- Quadratic variation
- Feller process
Special cases
- Random walk
- Bernoulli process
- Martingale
- Markov chain
- Poisson point process
- Gaussian process
- Semimartingale
- Feller process
- Submartingale
- Lévy process
- Diffusion process
- Stationary process
- Stopped process
- Supermartingale
- Self-similar process
- Local martingale
- Empirical process
- Adapted process