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The displayed continuous centered Gaussian process, with value zero at time zero, has the Brownian covariance kernel. Hence it is a Brownian motion in its own natural filtration. More generally, replacing three by $c$ gives covariance $s+c(c-3)(s/2-s^2/(6t))$ for $0<s\le t$, so three is the only nonzero valid coefficient. Its conditional future increment in the original Brownian filtration is $3(t-s)t^{-1}(s^{-1}{\int }_0^s{W}_{u}du-W_s)$, and is generally nonzero.

For a nonnegative continuous function $g$ on $[0,1]$ that vanishes at both endpoints, the head of the Brownian snake driven by $g$ is the centered Gaussian process $(Z_t{)}_{0\le t\le 1}$ with covariance functionConsequently $\mathbb{E}[(Z_t-Z_s{)}^2]=d_g(s,t)$, the pseudometric used by the real tree encoded by an excursion.

The canonical distance is the L2 norm of an increment of a Gaussian process. It is a pseudometric, since distinct parameters may represent equal random variables almost surely. Its metric covering number appears in the Dudley entropy integral. For standard fractional Brownian motion, $d(s,t)=|s-t{|}^H$; a normalization with twice the covariance function multiplies this distance by $\sqrt{2}$.

The circle-average process of a zero-boundary Gaussian free field in the unit disc has the law of a constant multiple of Brownian motion. It is a continuous centered Gaussian process with stationary increments and independent increments and starts at zero.

The continuum Gaussian free field on a planar domain $D$ is a centered Gaussian process indexed by finite-energy measures or test functions, with covariance given by the Green function of the Laplacian on $D$.

The covariance function of a second-order stochastic process $(X_t)$ isIt is a positive-semidefinite kernel and determines the finite-dimensional distributions of a centered Gaussian process.

If a continuous local martingale starts at zero and has deterministic continuous quadratic variation $[M{]}_t=f(t)$, the Dambis-Dubins-Schwarz theorem gives $M_t=B_{f(t)}$. It is therefore a centered Gaussian process. The converse follows from independent increments of a Gaussian martingale.

For a centered Gaussian process with covariance function $K(s-t)$, twice continuous differentiability of $K$ and differentiability of $K^{\prime \prime }$ at zero imply a differentiable modification of a stochastic process on compact intervals. The even function $K^{\prime \prime }$ satisfies $K^{\prime \prime \prime }(0)=0$, so the mean-square derivative $Y$ has variance of increments $2(K^{\prime \prime }(h)-K^{\prime \prime }(0))=o(|h|)$. A normal distribution has fourth absolute moment three times the square of its variance, so Kolmogorov continuity theorem gives a continuous modification of $Y$. The mean-square fundamental theorem of calculus identifies its path integral with a modification of a stochastic process of $X$. This argument does not require integrability of $K$ or continuity of $K^{\prime \prime \prime }$.

The stationary exponential covariance function isThe corresponding Gaussian process is Markov and is a stationary Ornstein-Uhlenbeck process.

A fractional Brownian motion with Hurst exponent $H\in (0,1)$ is a centered Gaussian process with the displayed standard covariance function and a continuous modification. Its increment variance is $|s-t{|}^{2H}$. Some sources omit the factor $1/2$, multiplying the process by $\sqrt{2}$; the corresponding increment variance is then $2|s-t{|}^{2H}$. Its canonical pseudometric of a Gaussian process is proportional to $|s-t{|}^H$, which gives finite expected value of its absolute supremum on compact intervals through the Dudley entropy integral.

Let $W(z)$ be a stationary real Gaussian process of mean zero and variance one, with autocorrelation $\rho (\zeta )$. The random phase screen $E(z)=e^{i\beta W(z)}$ has uncentered correlation $C_E(\zeta )=\mathbb{E}[E(z+\zeta )\overline{E(z)}]=e^{-{\beta }^2(1-\rho (\zeta ))}$. This follows from the characteristic function of the Gaussian difference, whose variance is $2(1-\rho (\zeta ))$. Joint Gaussianity is essential; normal one-point marginal distributions alone are insufficient.

A centered Gaussian process with almost surely continuous paths and covariance $\mathbb{E}[B_s{B}_t]=\min (s,t)$ is a standard Brownian motion.

Gaussian process classification links latent variables from a Gaussian process to class probabilities. A binary probit regression likelihood is ${\prod }_i\Phi ((2y_i-1)f_i)$ under conditional independence. The posterior distribution of the latent values is not a normal distribution.

For independent standard normal variables ${\xi }_i$ and ${\sum }_i{k}_i(t{)}^2<\infty$ at each fixed time, the series defines $X_t$ in $L^2$. Every finite linear combination is an $L^2$ limit of Gaussian variables and is Gaussian. Thus this constructs a Gaussian process with the Gram covariance kernel $K$. It supplies no sample-path continuity without additional assumptions.

For a zero-mean Gaussian process observed with independent Gaussian noise, the latent value at a new input is conditionally normal. Its mean is the kernel cross-covariance times the noisy kernel-matrix inverse times the observations, and its variance is the prior variance minus the corresponding quadratic form.

A Gaussian random field is a spatially indexed Gaussian process; its law is determined by its mean and covariance kernel.

A centered continuous Gaussian process that is also a martingale has independent increments. Every future increment has zero covariance with every finite vector of past values by the martingale property; uncorrelated jointly normal variables are independent.

An innovation measures the part of the current observation not predicted from past observations. The linear innovation process uses orthogonal projection onto the past linear span. A conditional expectation predictor can use nonlinear information; the two predictors agree for a Gaussian process.

The ordinary time integral of standard Brownian motion is a centered Gaussian process with continuously differentiable paths and derivative $B_t$. For $0\le s\le t$, its covariance is $\mathbb{E}(I_s{I}_t)=s^2(3t-s)/6$, obtained by integrating the Brownian covariance $\min (u,v)$. Consequently $I_t$ has normal distribution $N(0,t^3/3)$. This time integral is distinct from the Itô integral with respect to Brownian motion.

An isonormal Gaussian process over a real Hilbert space $H$ is a centered Gaussian process $(W(h){)}_{h\in H}$ satisfyingIt turns the geometry of $H$ into the covariance structure of a Gaussian family.