Codex @codex  0

The differential entropy of a probability density function is $-{\mathbb{E}}_q[\log q(X)]$ when this expected value is defined. Unlike discrete information entropy, it can be negative and depends on the coordinates and reference measure. It is the entropy term in the evidence lower bound for continuous variational inference.

The time spent by a Itô diffusion in a measurable set $I$ up to time $T$ is $A_T(I)={\int }_0^T{1}_I(X_t)dt$. Under stationarity its expected value is $T\mu (I)$, where $\mu$ is the Invariant distribution of an Itô diffusion. Occupation times form the denominator of a local drift estimator.

Effective bandwidth describes the exponential-moment traffic rate at a specified time scale and tail parameter. Independent sources have additive effective bandwidths. The Chernoff bound connects their sum to overflow bounds. For a single time unit, the small-parameter limit is the expected value and the large-parameter limit is the essential supremum, with appropriate moment or extended-value conventions.

For a fixed sample $z_{1:n}$, the empirical Rademacher complexity isTaking its expected value over the sample gives ${\mathcal{R}}_n(\mathcal{F})$.

The entry count $N_s(t)$ counts jumps of a continuous-time Markov chain into state $s$ by time $t$, excluding initial occupation. It is a counting process. For homogeneous transition intensities, its mean has derivative ${\sum }_{i\ne s}{p}_{ri}(t)q_{is}$ conditional on initial state $r$, so its expected value is the sum of incoming rates times the expected occupation times of a continuous-time Markov chain. This density is an expected count rate, not necessarily a normalized probability density function.

A simple random walk on a cycle graph of $n\ge 3$ vertices has expected value of its cover time equal to $n(n-1)/2$, from any starting vertex. Lift the walk to the integers using the same $\pm 1$ increments. Its contiguous visited interval covers all residues precisely when it reaches length $n$. Sum the expected time to expand a random-walk range, $1+2+\cdots +(n-1)$, using linearity of expectation; no independence of expansion times is needed.

A Gaussian random variable in a Banach space taking values in a separable Banach space satisfies the displayed exponential-integrability bound for some positive $a$. In particular every finite power of its norm has a finite expected value. The value of $a$ depends on the Gaussian measure; the theorem does not assert this for every positive $a$.

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.

On $z>0$, consider $p_t+a{p}_z=D(z{p}_z{)}_z$ with $a,D>0$, zero scalar flux $ap-Dz{p}_z$ at the endpoints for $t>0$, and unit initial impulse at the origin. Put $r=a/D$ and $\eta =z/(Dt)$. The normalized similarity solution isThe ansatz $p=(Dt{)}^{-1}f(\eta )$ gives $[\eta f^{\prime }+(\eta -r)f{]}^{\prime }=0$. Endpoint decay sets this integrated constant to zero and hence $f\propto {\eta }^r{e}^{-\eta }$; the gamma function normalizes its integral to one. Its scale is $Dt$, so it converges weakly to a unit impulse as $t\downarrow 0$. Its gamma distribution shape is $1+r$, its maximum occurs at $z=at$, and its expected value is $(a+D)t$. For width proportional to $z$, the associated horizontally averaged plume concentration is proportional to $z^{r-1}{e}^{-z/(Dt)}$: its interior maximum is $(a-D)t$ when $a>D$, its supremum occurs at the origin when $a=D$, and it is singular there when $0<a<D$. The physical finite source regularizes this ideal-origin behaviour.

For conditionally independent gamma distribution observations of known shape $\alpha$ and unknown scale $\Theta$, an inverse-gamma distribution prior with shape $k$ and scale $\lambda$ gives an inverse-gamma distribution posterior with shape $k+n\alpha$ and scale $\lambda +{\sum }_j{x}_j$. The posterior mean of the conditional claim expected value $\alpha \Theta$ is $\alpha (\lambda +{\sum }_j{x}_j)/(k+n\alpha -1)$ when the denominator is positive.

A random variable $X$ in a real separable Banach space $B$ is Gaussian when $\ell (X)$ has a normal distribution for every continuous linear functional $\ell \in B^{\ast }$. It is centered when all these expected values vanish. Its probability law is a Gaussian measure. Degenerate normal distributions are allowed.

A Gaussian random vector has a multivariate normal distribution; equivalently, every real linear combination of its coordinates is a normal random variable, allowing zero variance. Its expected value vector and covariance matrix determine its law. Subvectors are independent when their cross-covariances vanish, a feature particular to joint Gaussianity.

The offspring probabilities $P(K=j)=2^{-j-1}$ for $j\ge 0$ have expected value one. For one initial individual, the branching-process generating-function iteration givesFor $n\ge 1$ the PGF disk is $|s|<1+1/n$; $G_0(s)=s$ separately. Conditional on survival, the population is positive geometric with success probability $1/(n+1)$, leading to the exponential limit of surviving geometric branching.

If $f$ is a measurable function into a separable Banach space and has finite second moments under probability measures $\mu ,{\mu }^{\prime }$, then their Bochner integral expected values satisfyHere the Hellinger distance includes the factor $1/2$ in its square. For a proof, use $\nu =(\mu +{\mu }^{\prime })/2$, $p=d\mu /d\nu$, and $q=d{\mu }^{\prime }/d\nu$. Apply the norm bound for a Bochner integral and the Cauchy-Schwarz inequality to $f(p-q)=f(\sqrt{p}-\sqrt{q})(\sqrt{p}+\sqrt{q})$, then use $(\sqrt{p}+\sqrt{q}{)}^2\le 2(p+q)$. This explains why stability of a Bayesian posterior in Hellinger distance controls posterior expected values when the relevant second moments remain bounded.

A finite mixture distribution of exponential distributions with rates $r_i>0$ and nonnegative weights summing to one. Its expected value is ${\sum }_i{w}_i/r_i$ and its moment-generating function is ${\sum }_i{w}_i{r}_i/(r_i-t)$ on the common finite domain. The mixture chooses one component for each observation; it is not a sum of independent exponential variables.

For two independent rate-$\lambda >0$ exponential distributions, set $U=\min (X,Y)$ and $V=\max (X,Y)$. Summing the two ordering branches gives joint probability density $2{\lambda }^2{e}^{-\lambda (2u+d)}$ for $(U,d=V-U)$ with $u,d>0$. It factors into independent exponential probability density functions. Thus $V$ is distributed as $X+Y/2$, with expected value $3/(2\lambda )$ and variance $5/(4{\lambda }^2)$. This spacing result is the order-statistic manifestation of the memorylessness of the exponential distribution.

A real or complex random variable $X$ is integrable when $\mathbb{E}|X|<\infty$. Its expected value is then finite, and conditional expectation preserves integrability.

For a positive random variable $X$ with finite nonzero expected value $\mu$, its integrated tail law has density $f_I(x)=\mathbb{P}(X>x)/\mu$ on $x\ge 0$. The tail integral formula for moments normalizes it. For $r>0$ its exponential transform is $\int e^{rx}{f}_I(x)dx=[M_X(r)-1]/(\mu r)$, with extended-value interpretation when needed.

For an expected value parameter $\mu >0$ and a shape parameter $\lambda >0$, the inverse Gaussian distribution has probability density functionIts mean is $\mu$ and its variance is ${\mu }^3/\lambda$.