Add to ORKGStochastic Process A stochastic or random process {Zt}, · · ·,−1,0,1, · · ·, is a collection of random variables, real or complex-valued, defined on the same probability space. Gaussian Process: A real-valued process {Zt}, t ∈ T, is called Gaussian process if for all t1, t2, · · · , tn ∈ T, the joint distribution of (Zt1, Zt2, · · · , Ztn) is multivariate nor-mal. The finite dimensional distributions of a Gaussian pro-cess are completely determined from: m(t) = E[Zt] and R(s, t) = Cov[Zs, Zt]
Given a Gaussian Process with a zero mean and a Squared Exponential (SE) kernel. We are interested i...
One contribution of 17 to a Discussion Meeting Issue ‘Signal processing and inference for the physic...
With the Gaussian Process model, the predictive distribution of the output corresponding to a new gi...
We first define several words. A stochastic process {Yt: t ≥ 0} is • stationary if, for all t1 < ...
I A stochastic process is a family of random variables X (t), t ∈ T indexed by a parameter t in an i...
• A Gaussian distribution depends on a mean and a covariance vector / matrix. • A Gaussian process d...
and non-Gaussian processes of zero power variation, and related stochastic calculus
We find an explicit formula for the first passage probability, Qa(T|x) = Pr(S(t) \u3c a, 0 ≦ t ≦ T |...
International audienceSoit $\{X_{t}, t\in[0,1]\}$ un processus gaussien stationnaire centr{é}, d{é}f...
We provide a detailed derivation of the Karhunen–Loève expansion of a stochastic process. We also d...
Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert spa...
Here the author shows how to remove the inconsistencies in Random Variable theory by introducing the...
textabstractIn this paper we investigate the tail behaviour of a random variable S which may be view...
Abstract. Gaussian processes are a natural way of dening prior distributions over func-tions of one ...
Nous présentons dans cette contribution, un formalisme général concernant les procédures temporelles...
Given a Gaussian Process with a zero mean and a Squared Exponential (SE) kernel. We are interested i...
One contribution of 17 to a Discussion Meeting Issue ‘Signal processing and inference for the physic...
With the Gaussian Process model, the predictive distribution of the output corresponding to a new gi...
We first define several words. A stochastic process {Yt: t ≥ 0} is • stationary if, for all t1 < ...
I A stochastic process is a family of random variables X (t), t ∈ T indexed by a parameter t in an i...
• A Gaussian distribution depends on a mean and a covariance vector / matrix. • A Gaussian process d...
and non-Gaussian processes of zero power variation, and related stochastic calculus
We find an explicit formula for the first passage probability, Qa(T|x) = Pr(S(t) \u3c a, 0 ≦ t ≦ T |...
International audienceSoit $\{X_{t}, t\in[0,1]\}$ un processus gaussien stationnaire centr{é}, d{é}f...
We provide a detailed derivation of the Karhunen–Loève expansion of a stochastic process. We also d...
Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert spa...
Here the author shows how to remove the inconsistencies in Random Variable theory by introducing the...
textabstractIn this paper we investigate the tail behaviour of a random variable S which may be view...
Abstract. Gaussian processes are a natural way of dening prior distributions over func-tions of one ...
Nous présentons dans cette contribution, un formalisme général concernant les procédures temporelles...
Given a Gaussian Process with a zero mean and a Squared Exponential (SE) kernel. We are interested i...
One contribution of 17 to a Discussion Meeting Issue ‘Signal processing and inference for the physic...
With the Gaussian Process model, the predictive distribution of the output corresponding to a new gi...