BrownianBridgeProcess
BrownianBridgeProcess[σ,{t1,a},{t2,b}]
represents the Brownian bridge process from value a at time t1 to value b at time t2 with volatility σ.
BrownianBridgeProcess[{t1,a},{t2,b}]
represents the standard Brownian bridge process from value a at time t1 to value b at time t2.
BrownianBridgeProcess[t1,t2]
represents the standard Brownian bridge process pinned at 0 at times t1 and t2.
represents the standard Brownian bridge process pinned at 0 at time 0 and at time 1.
Details
- BrownianBridgeProcess is also known as pinned Brownian motion process.
- BrownianBridgeProcess is a continuous-time and continuous-state random process.
- The state for a Brownian bridge process satisfies and .
- The state follows NormalDistribution[a+(b-a) (t-t1)/(t2-t1),].
- The parameters σ, t1, t2, a, and b can be any real numbers, with σ positive and t2 greater than t1.
- BrownianBridgeProcess can be used with such functions as Mean, PDF, Probability, and RandomFunction.
Examples
open allclose allBasic Examples (3)
Scope (13)
Basic Uses (8)
Process Slice Properties (5)
Univariate SliceDistribution:
First-order probability density function:
Multivariate slice distribution:
Second-order PDF:
Compute the expectation of an expression:
Calculate the probability of an event:
Skewness and kurtosis are constant:
CentralMoment and its generating function:
FactorialMoment has no closed form for symbolic order:
Cumulant and its generating function:
Properties & Relations (9)
A Brownian bridge is not weakly stationary:
A Brownian bridge process does not have independent increments:
Compare to the product of expectations:
Conditional cumulative probability distribution:
A Brownian bridge process is a special ItoProcess:
As well as StratonovichProcess:
A Brownian bridge process is a solution to the stochastic differential equation :
Compare with the corresponding smooth solution:
A Brownian bridge process initially follows its corresponding WienerProcess:
The distribution of the absolute supremum of a BrownianBridgeProcess follows a Kolmogorov distribution:
Compare the cumulative histogram with the cumulative distribution function of a Kolmogorov distribution:
BrownianBridgeProcess can be simulated directly from WienerProcess:
Apply a transformation to the random sample:
Compare to the corresponding BrownianBridgeProcess:
A Brownian bridge is a conditioned WienerProcess:
Compare to the CDF of the slice distribution of a Brownian bridge:
Possible Issues (1)
Neat Examples (3)
Text
Wolfram Research (2012), BrownianBridgeProcess, Wolfram Language function, https://reference.wolfram.com/language/ref/BrownianBridgeProcess.html.
CMS
Wolfram Language. 2012. "BrownianBridgeProcess." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/BrownianBridgeProcess.html.
APA
Wolfram Language. (2012). BrownianBridgeProcess. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/BrownianBridgeProcess.html