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For fixed positive integer $r$, the failures-before-the-$r$th-success distribution is a one-parameter exponential family withand carrier $h(x)=\left(\genfrac{}{}{0ex}{}{x+r-1}{x}\right)$.

The deviance from ${\theta }_1$ to ${\theta }_2$ is twice the Kullback-Leibler divergence:

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 ${\mu }^3/\lambda$.

The cumulant function, or log-partition function, is the normalizing termwith a sum in the discrete case.

In the canonical representation$\theta$ is the natural parameter and $T$ is the natural statistic.

Every finite subgroup of $K^{\times }$ is cyclic. If its exponent is $m$, all its elements are roots of $X^m-1$, so its size is at most $m$; finite abelian group theory supplies an element of order $m$, forcing equality.

For $q>0$ and a real random variable $X$, the tail integral formula for moments isvalid even if both sides are infinite. Pointwise, $|X{|}^q={\int }_0^{|X|}q{t}^{q-1}dt$; Tonelli theorem interchanges this nonnegative integral with expectation. Replacing $>$ by $\ge$ does not change the integral, since their difference is confined to the at most countably many atoms of the distribution. For an exponential random variable of rate $\lambda$, the formula at $q=2$ gives $\mathbb{E}{X}^2=2{\int }_0^{\infty }t{e}^{-\lambda t}dt=2/{\lambda }^2$.

Let $X_1,\dots ,X_n$ be an IID sample of real-valued random variables with finite expected value $\mu$ and finite variance ${\sigma }^2$, where $n\ge 1$. Its sample mean $\overline{X}=n^{-1}{\sum }_i{X}_i$ satisfiesLinearity of expectation gives the first identity. The variance of a sum and vanishing cross covariances give $\mathrm{Var}({\sum }_i{X}_i)=n{\sigma }^2$; scaling by $1/n$ scales the variance by $1/n^2$. Pairwise independent random variables with the same expected value and variance suffice for these identities, even without full independence. Grinstead and Snell state the corresponding finite-variance independent-trials result.^[ref]

The standard deviation of the sample mean is $\sigma /\sqrt{n}$. Four times as many observations halve this scale; they do not divide it by four. In the fair Bernoulli distribution example, $\sigma =1/2$, so sample sizes $4$ and $64$ give standard deviations $1/4$ and $1/16$, respectively. The common-coin averaging example shows why merely repeating observations with a common marginal law does not justify this calculation.

Chebyshev's inequality turns the variance identity into a probability bound: for $\epsilon >0$,This proves convergence in probability of the sample mean to $\mu$ in this finite-variance setting, a form of the weak law of large numbers. It is an upper bound, so values above one convey no improvement over the universal probability bound one. Cauchy sample mean stability lies outside these hypotheses.

For integrable random variables $X_1,\dots ,X_n$ on the same probability space and fixed real numbers $a_1,\dots ,a_n$, linearity of expectation statesThe random variables can be dependent random variables. The formula follows by the linearity of integration; a finite linear combination of integrable random variables remains integrable. For example, if $X$ takes $-1,2,8$ with probabilities $1/2,3/10,1/5$, then $X$ and $2X$ are dependent random variables, yet $\mathbb{E}[X+2X]=3\mathbb{E}[X]=5.1$.

An especially useful application counts how many events occur. Put $I_i=1_{A_i}$, an indicator random variable, and $N={\sum }_i{I}_i$. Since $\mathbb{E}[I_i]=\mathbb{P}(A_i)$, we get $\mathbb{E}[N]={\sum }_i\mathbb{P}(A_i)$. There is no need to compute the whole probability distribution of $N$, or to assume independence. For a uniform random permutation of $n\ge 1$ objects, each object stays fixed with probability $1/n$, so the fixed point count of a uniform random permutation has expected value one for every $n$. ^[ref].

Linearity of expectation concerns addition and multiplication by fixed scalars. Products instead satisfy $\mathbb{E}[XY]=\mathbb{E}[X]\mathbb{E}[Y]$ under the hypotheses of expectation of a product of independent random variables. For the example above, $\mathbb{E}[X^2]=14.5$, whereas $(\mathbb{E}[X]{)}^2=2.89$. Applying a nonlinear function before averaging can therefore change the answer substantially; use the law of the unconscious statistician to compute that average.

The tail-sum formula expresses an expected value as the area under a survival function. For every nonnegative random variable $Y$,This Lebesgue integral and the expected value may both be $+\infty$; a probability density function is unnecessary. For a random variable $N$ taking values in the nonnegative integers, the corresponding sum isThe events $\{N>k\}$ and $\{N\ge k+1\}$ coincide because $N$ takes integer values. Thus the strict sum starts at zero, while the inclusive sum starts at one.^[ref]

For an original example, let the probability mass function of $N$ beIts expected value computed directly is $0(1/5)+1(3/10)+3(1/2)=9/5=1.8$. The strict tail probabilities at thresholds $0,1,2,3$ are $4/5,1/2,1/2,0$: their sum is again $1.8$. There is no probability mass at two, yet the event $\{N>2\}$ still has probability $1/2$. The survival function has heights $4/5,1/2,1/2$ on the successive intervals $[0,1),[1,2),[2,3)$; each interval has length one, so its integral is the same sum.

The indexing matters numerically. Starting the inclusive sum at zero adds $\mathbb{P}(N\ge 0)=1$, giving $2.8$ for this example. Starting the strict sum at one omits $\mathbb{P}(N>0)=4/5$, giving $1$. For a general nonnegative random variable, replacing $>$ with $\ge$ inside the integral is harmless: the difference is $\mathbb{P}(Y=t)$, nonzero at only a countable set of thresholds. For each positive integer $m$, at most $m$ thresholds can have probability mass at least $1/m$; their countable union contains all thresholds with positive probability mass and has Lebesgue measure zero. This reasoning concerns an integral, not equality of the two survival function conventions at every point.

For a continuous original example, take the uniform distribution on the interval $[0,4]$. Its survival function is $S(t)=1-t/4$ for $0\le t\le 4$ and zero for $t>4$. The area is a triangle of base four and height one:Directly integrating against the probability density function gives the same expected value, ${\int }_0^{4}y(1/4)dy=2$. The integral of the probability density function alone is one; it is the survival function whose area is the expected value.

The proof counts layers of each outcome. For a realized value $y\ge 0$, the indicator function $1_{\{y>t\}}$ equals one over an interval of length $y$, soTonelli theorem permits the interchange because the indicator function is nonnegative; no finite expected value is assumed. Similarly, an outcome $N=n$ contributes one indicator random variable for each of the $n$ events $\{N\ge k\}$ that occur:Again Tonelli theorem applies to the nonnegative sum, or equivalently the monotone convergence theorem applies to its partial sums. This exchanges counting layers within each outcome with averaging across outcomes. The existing integer-valued hypothesis in the tail-sum formula explains what changes when the values are not integers; the tail integral formula for moments weights the layers to obtain higher moments.

For an integrable random variable $X$ that can have either sign, apply the nonnegative formula separately to its positive part $X_{+}=\max (X,0)$ and negative part $X_{-}=\max (-X,0)$:Integrability makes both integrals finite. More generally the difference defines an extended expected value when at least one is finite; if both are infinite, subtraction does not define an expected value.^[ref]

Tail probabilities are often easier to measure or bound than a whole probability density function. For a device's survival time, the survival function records the probability of still operating at each threshold; its integral gives the mean operating time. A finite observation window naturally gives the expected value of a capped nonnegative random variable. For a mathematical bound, $\mathbb{P}(Y>t)\le e^{-t/2}$ for all $t\ge 0$ implies $\mathbb{E}[Y]\le {\int }_0^{\infty }{e}^{-t/2}dt=2$. The tail-sum formula turns control of a whole family of events into control of an average.

An indicator random variable is the indicator function of an event $A$ in a probability space: $1_A$ is one when $A$ occurs and zero otherwise. It has a Bernoulli distribution with parameter $p=\mathbb{P}(A)$, so $\mathbb{E}[1_A]=p$ and $\mathrm{Var}(1_A)=p(1-p)$. Even an event described without numbers becomes a numerical random variable in this way.

For a finite collection of events, their indicator random variables add up to the number that occur. Linearity of expectation therefore turns an average count into a sum of probabilities, without requiring independence. The fixed point count of a uniform random permutation provides an example where the indicators are dependent random variables.

The variance measures the mean squared distance of a random variable from its expected value. For a real square-integrable random variable $X$,The square prevents positive and negative deviations from cancelling. It also gives the variance squared units: if $X$ measures a length in metres, its variance has units of square metres. Its nonnegative square root is the standard deviation, which has the same units as $X$.

For example, let $X$ take $-1$, $0$ and $1$ with equal probabilities. Its expected value is zero, but its variance is $[(-1{)}^2+0^2+1^2]/3=2/3$, and its standard deviation is $\sqrt{2/3}$. The constant random variable zero has the same expected value and zero variance: a mean alone does not describe the spread. The variance is zero exactly when $X=\mathbb{E}X$ almost surely.

The variance is useful because squared deviations interact neatly with linear combinations. The covariance supplies the cross terms when combining different random variables, and a covariance matrix organizes those terms for many variables. The characterization of variance as the minimum mean squared error of a constant also explains why the expected value is the best constant prediction under squared error.

An exoplanet transmission spectrum measures a wavelength-dependent transit depth. In a simple opaque-disk geometry with uniform stellar brightness, an effective planetary radius $R_p$ gives $D=(R_p/R_{\ast }{)}^2$, where $R_{\ast }$ is the stellar radius. If stronger atmospheric absorption increases that radius by $\Delta R=N_{H}H$, define $H$ as the atmospheric scale height and $N_H$ as the number of scale heights separating the two spectral levels. The additional occulted annulus givesThe last approximation requires $N_{H}H\ll R_p$. For instance, $R_p/R_{\ast }=0.1$ and $N_{H}H/R_p=0.05$ give exact $\Delta D=0.001025$ and leading estimate $0.001$; these are stipulated geometric ratios.

Since $H=k_{B}T/(\mu m_{H}g)$ in the thin isothermal atmosphere model, larger temperature, smaller mean molecular weight or weaker gravity can increase the signal. The factor $N_H$ is set by the atmospheric opacity, composition and cloud structure; it is not a universal constant. Limb darkening, stellar heterogeneity and instrumental effects also affect the observed transit depth.^[ref]

Atmospheric escape is the loss of gas from a planet to space. In Jeans escape, particles in the high-speed tail of a velocity distribution leave a nearly collisionless upper atmosphere if they exceed the local escape velocity. Hydrodynamic escape instead treats a collisional bulk outflow. Photochemical escape supplies another route by creating energetic reaction products.

Which description applies depends on gravity, heating, composition and the collision rate, rather than only on the planetary equilibrium temperature. Absorption of energetic stellar radiation can heat high atmospheric layers far above that temperature. Removing a light envelope changes the measured radius and future exoplanet atmosphere, connecting escape to the interpretation of a planetary mass-radius relation.

An exoplanet emission spectrum describes the planetary thermal radiation as a function of wavelength. In an opaque exoplanet atmosphere, emerging light at a given wavelength comes predominantly from layers near outward optical depth of order one. Changing molecular opacity changes the layers sampled, so the spectrum jointly encodes composition and the atmospheric pressure-temperature profile.

A strong band is commonly darker when the sampled upper layers are cooler than the continuum-emitting layers. An atmospheric thermal inversion can instead put the band as an emission line. Occultation spectroscopy isolates the dayside spectrum, while exoplanet direct imaging can obtain it by spatially separating planetary and stellar light. A brightness temperature assigned to each band is a useful description of the data, but different bands can correspond to different physical layers.

An exoplanet transmission spectrum is the measured exoplanet transit depth as a function of wavelength. Light from the star passing through the limb of an exoplanet atmosphere is absorbed or scattered differently in different spectral bands. At a strongly absorbing wavelength, a ray becomes opaque farther from the centre, making the effective planetary radius larger.

The annulus model for transmission spectroscopy estimates the extra blocked area, while the slant optical depth of an isothermal atmosphere explains why this method can detect tenuous gas. Molecular-band amplitudes depend on the atmospheric scale height, abundances, and exoplanet cloud deck. Stellar contamination of a transmission spectrum and reference-pressure degeneracy in transmission spectroscopy limit how directly this observable can be translated into a unique composition.

An exoplanet secondary eclipse occurs when a planet passes behind its host star. Immediately outside the event the detector receives light from both bodies; during complete occultation it receives only stellar light. Their difference isolates planetary light, consisting of reflected stellar radiation, planetary thermal radiation, or both. Its fraction of the combined out-of-eclipse light is the eclipse depth.

Measuring that depth across wavelengths gives occultation spectroscopy of the visible dayside. The thermal eclipse depth connects a simple blackbody model to the radius ratio and brightness temperature. At visible wavelengths, reflected light can instead constrain geometric albedo. Changes in the host star or detector response must be modelled because the planetary signal is a small difference between two much brighter measurements.

An irradiated planetary atmosphere receives stellar radiation from above while intrinsic planetary heat flows outward from below. Stellar heating is therefore deposited where incident light is absorbed, rather than being supplied entirely at the bottom. Different opacities for stellar and planetary radiation can produce a deep, nearly isothermal region or an atmospheric thermal inversion.^[ref]

The absorbed power sets a global energy constraint, summarized by the planetary equilibrium temperature. The vertical atmospheric pressure-temperature profile additionally requires radiative transfer, convection and the heating depth. On a exoplanet under tidal locking, day-night heat redistribution also matters because only one hemisphere receives direct starlight.

A first excited state has the lowest allowed energy eigenvalue strictly above the ground-state energy, when such a next eigenvalue exists. The level can be degenerate, in which case several independent first excited states share that energy.

For a quantum harmonic oscillator, the ground state has occupation $n=0$ and the first excited state $n=1$, separated by $\hslash \omega$. In an infinite square well labeled by $n=1,2,\dots$, the first excited state instead has label $n=2$. “First” orders the energies rather than imposing a universal index convention. A system with a continuum starting at its lowest energy need not possess a normalizable first excited state at all.

The eigenvalues of $\left(\begin{array}{cc}{E}_1& h\\ h& E_2\end{array}\right)$ are their mean plus or minus $\sqrt{(E_2-E_1{)}^2/4+h^2}$, so nonzero coupling increases their separation.