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For independent affected sib pairs with IBD counts $n_0,n_1,n_2$, the likelihood function of the recombination fraction is proportional to $s^{2n_0+n_1}(1-s{)}^{2n_2+n_1}$, with $s=2\theta (1-\theta )$. At fixed sample size, the total shared-copy count $2n_2+n_1$ is a sufficient statistic by the Fisher-Neyman factorization theorem. Thus a one-sided likelihood-ratio test has the same ordering as a test for excess mean IBD sharing. This equivalence depends on the stated fully penetrant recessive model and does not extend to arbitrary disease models.

// Target: biology.bigb

For a sufficient statistic $(C,S)$ and parameter $(\psi ,\lambda )$, a useful strong reduction requires the law of $S$ to be free of $\psi$ and the conditional distribution of $C$ given $S$ to be free of the nuisance parameter $\lambda$. In the discrete case it giveswhere ${\varphi }_0$ sums to one on each $(C,S)$ fiber, ${\varphi }_S$ sums to one over $s$, and ${\varphi }_C$ sums to one over $c$ separately for every fixed $s$. Ordinary ancillarity, or just the law of $S$ being free of $\psi$, does not by itself guarantee that conditioning removes $\lambda$.

Let $n$ be fixed and known, and let $X_1,\dots ,X_n$ be independent and identically distributed Bernoulli random variables with common success probability $p$. Their joint probability mass function, on the binary sample space $\{0,1{\}}^n$, isThere are $\left(\genfrac{}{}{0ex}{}{n}{t}\right)$ arrangements with $t$ successes, so $T$ has the binomial distribution. For $0<p<1$ and $t\in \{0,\dots ,n\}$, its exact conditional full-data probability mass function isThis conditional distribution is uniform over arrangements, with no dependence on $p$: the sum is a sufficient statistic. ^[ref]

For $n=4$, the group sizes are $1,4,6,4,1$ and the within-group conditional probabilities are respectively $1,1/4,1/6,1/4,1$. For example, $T=2$ leaves exactly $0011,0101,0110,1001,1010,1100$, each with conditional probability $1/6$. At $p=1/4$, each such sequence has unconditional probability $9/256$ and the group has probability $54/256$. At $p=1/2$, these values are $1/16$ and $6/16$; at $p=3/4$ they return to $9/256$ and $54/256$. The conditional weights stay $1/6$ in all three cases.

At the boundary $p=0$, only $T=0$ has positive probability; at $p=1$, only $T=n$ does. Dividing by $P_p(T=t)$ on the other groups would divide by zero. Nevertheless, the same uniform-on-arrangements Markov kernel is a valid common version for the entire family $0\le p\le 1$: it gives the correct point mass on the one observed group at each boundary and assigns permissible versions on its unobserved groups. Define the joint probability mass function there by the original product of the one-trial Bernoulli probability mass functions, or use the limiting convention $0^0=1$ in the compact formula.

The sample mean $T/n$ is an unbiased estimator of $p$ and retains the sufficient statistic because $n$ is known. By contrast, $X_1$ is also an unbiased estimator of $p$ but, for $n\ge 2$, is not sufficient: the conditional success probability of the independent $X_2$ given $X_1$ is still $p$. Thus unbiasedness does not imply sufficiency.

Sufficiency also does not imply completeness of a statistic. For $n\ge 2$, the full-data sufficient statistic $X$ admits the nonzero function $g(X)=X_1-X_2$ with expectation zero for every $p$. Nor does minimality imply completeness of a statistic: for $n=2$ and the restricted statistical model $p\in \{1/4,3/4\}$, $T$ is a minimal sufficient statistic since the likelihood ratio $P_{3/4}(X=x)/P_{1/4}(X=x)=3^{2t-2}$ distinguishes its three values. Yet $g(0)=3,g(1)=-5,g(2)=3$ has expectation $(27-30+3)/16=0$ at both parameter values and is never zero. Changing the parameter family can change completeness of a statistic even while preserving sufficiency.

Bounded completeness requires the defining implication for a complete statistic only for bounded measurable functions. Completeness implies it, but the converse need not hold. If a sufficient statistic $T$ gives a parameter-independent event probability $c$ and the conditional probability $S(T)$ of that event is nonconstant, then $S-c$ is a bounded zero-mean obstruction to bounded completeness.

A sufficient statistic is complete when every integrable function of it with expectation zero for every parameter is almost surely zero for every parameter. Full regular one-parameter exponential families with natural parameter ranging over an open interval provide a standard completeness result. Completeness makes an unbiased function of a sufficient statistic unique, giving the Lehmann–Scheffé theorem.

For independent multinomial distributions with fixed stratum totals $N_s$ and baseline logits $\log (p_{sl}/p_{sL})=x_s^T{\beta }_l$, the sufficient statistics are the displayed response-specific design-weighted counts, $l<L$. Expanding the log-likelihood gives ${\sum }_{l<L}{\beta }_l^T{T}_l-{\sum }_s{N}_s\log (1+{\sum }_{l<L}{e}^{x_s^T{\beta }_l})$, plus a data-only term. The Fisher-Neyman factorization theorem therefore proves sufficiency; with an unrestricted identifiable canonical parameter, a likelihood-ratio argument proves minimal sufficiency. Factor indicators give the corresponding response/covariate margins.

For independent $N(\mu ,{\mu }^2)$ observations with $\mu >0$, the sufficient statistics are their sum and squared norm. Condition on $a=\sqrt{n}\Vert y\Vert /{\sum }_i{y}_i>0$ and write $v=\hat{\mu }=\Vert y\Vert /(q\sqrt{n})$, where $q=(\sqrt{1+4a^2}+1)/(2a)$. Polar coordinates supply radial measure $\Vert y{\Vert }^{n-1}d\Vert y\Vert$, while the conditional angular factor does not depend on $\mu$. This proves the displayed shape. Its normalization depends on $a,n$ but not $\mu$ after the substitution $v=\mu z$, and agrees with the normalized P-star approximation.

For a finite-valued random variable, an epsilon-sufficient set $S$ retains probability at least $1-ϵ$. This notion is about covering probability mass and is unrelated to a sufficient statistic. For an IID random variable source, any such set of words with fixed $ϵ<1$ requires exponentially many words at a rate approaching the Shannon entropy.

In a parametric statistical model, the likelihood function compares parameter values, while sampling distributions calibrate tests and confidence intervals. A sufficient statistic retains the parameter information. Conditioning on an observed ancillary statistic restricts that calibration to experiments with the same ancillary configuration, instead of averaging over configurations whose distribution is unrelated to the parameter. Since the ancillary's marginal law is parameter-free, conditional and unconditional likelihoods have the same parameter-dependent factor. With a nuisance parameter, a conditional law or pivotal quantity must remove it before inference for the parameter of interest can use a nuisance-free reference law.

Assume the full-data sample space and the range of the statistic $T$ are standard Borel spaces, $T$ is a measurable function, and the statistical model $\{P_{\theta }\}$ is dominated by one common sigma-finite measure $\nu$. Write $f_{\theta }=d{P}_{\theta }/d\nu$ for its densities. Then $T$ is a sufficient statistic if and only if there exist nonnegative measurable functions $g_{\theta }$ and $h$, with $h$ independent of $\theta$, such thatThe statistical parameter can enter $g_{\theta }$ only through $\theta$ and the observed $T(x)$. ^[ref]

For discrete data, take counting measure; for ordinary continuous data, take Lebesgue measure. Independence, identical sampling laws, smooth dependence on $\theta$, and a fixed positive support of a probability distribution are not requirements of this theorem. Parameter-dependent support must nevertheless be included in $f_{\theta }$, usually through an indicator function, and must fit the displayed factorization. The sufficient statistic for a symmetric uniform sample illustrates exactly this: its indicator function depends on the unknown scale through the observed absolute maximum. The null sets in the theorem concern versions of densities; the factors are not generally themselves normalized probability distributions.

For a discrete sample space, the cancellation behind the theorem is particularly transparent. On a group with positive probability, factorization givesConversely, multiply a common conditional probability mass function by $P_{\theta }(T=T(x))$ to obtain the required factorization. For continuous data, a sum over a group is not a density integral over the original Lebesgue measure: the group may have measure zero. Use the regular conditional distribution formulation and the Fisher-Neyman factorization theorem instead of that discrete calculation.

Insurance exposure scales the opportunity for claims: $w_j$ policy-years produce expected count $w_j\Theta$ at common annual frequency $\Theta$. Conditionally independent insurance exposures add, so the total count and total insurance exposure are sufficient statistics in a Poisson distribution frequency model. A yearly claim average has conditional expectation independent of the number of policies, but its conditional variance falls with that number.

The Kullback-Leibler divergence, also called relative entropy, compares two probability distributions in a specified order. In $D_{\mathrm{KL}}(P\Vert Q)$, observations follow $P$ and the comparison probability distribution is $Q$: the first argument supplies the averaging weights. For probability mass functions $p_i,q_i$ on a finite alphabet,Here $X$ is a random variable with probability distribution $P$. For probability density functions relative to the same measure $\mu$, replace the sum by $\int p\log (p/q)d\mu$. These formulas use natural logarithms unless stated otherwise. The expected value is under $P$, even though $Q$ appears in the denominator. ^[ref]

Take $P=(0.75,0.25)$ and $Q=(0.5,0.5)$ on two outcomes. Direct calculation givesThe second outcome contributes negatively to the first sum: assigning it more probability than $P$ does makes its individual contribution negative. Nevertheless, relative entropy nonnegativity guarantees a nonnegative total. Reversing the arguments changes both the likelihood ratio and the averaging probabilities; taking an absolute value inside the logarithm would define a different quantity.

The units depend on the logarithm: natural logarithms give nats, whereas base-two logarithms give bits. Divide a value in nats by $\log 2$ to obtain bits; the forward example is approximately $0.188722$ bits. Changing the base rescales every Kullback-Leibler divergence by the same positive factor. Since the two calculated directions differ, Kullback-Leibler divergence is asymmetric and cannot be a metric. It also need not satisfy the triangle inequality. ^[ref]

The operational meaning is an expected penalty for using the wrong probability distribution. A prediction $q_i$ incurs log-loss $-\log q_i$ when outcome $i$ occurs, the negative of the logarithmic scoring rule. Under $P$, its expected value is the cross-entropy $H(P,Q)=-{\sum }_i{p}_i\log q_i$. Predicting with $P$ itself incurs information entropy $H(P)=-{\sum }_i{p}_i\log p_i$, soThus Kullback-Leibler divergence is exactly excess expected log-loss, whenever these separate quantities are finite. ^[ref]

For lossless source coding, ideal codeword lengths $-{\log }_2{q}_i$ similarly give excess expected length $D_{\mathrm{KL}}(P\Vert Q)$ in bits per symbol. Actual prefix codes require integer codeword lengths; Shannon's source coding theorem explains how long blocks approach ideal per-symbol lengths. The interpretation concerns an average, not a fractional physical bit attached to every outcome. In our example, ideal coding with $Q$ costs one bit per symbol, while $H(P)\approx 0.811278$ bits; the difference is exactly the computed $0.188722$ bits. ^[ref]

Zeros require explicit conventions. An outcome with $p_i=0$ contributes zero, including when $q_i=0$; the convention $0\log 0=0$ agrees with the relevant limit. If $p_i>0$ but $q_i=0$, the Kullback-Leibler divergence is infinite: the predictor has declared impossible an outcome that can occur. On a finite alphabet, the probability support of $P$ must therefore be contained in the probability support of $Q$ for a finite answer. For example, $P=(1,0)$ and $Q=(0.5,0.5)$ give $D(P\Vert Q)=\log 2$, but $D(Q\Vert P)=+\infty$. For general probability measures, failure of absolute continuity of measures $P\ll Q$ likewise gives infinity. Even $P\ll Q$ does not guarantee a finite integral. ^[ref]

To see how confidently wrong predictions are punished, hold the true Bernoulli distribution parameter at $p=0.75$ and vary the predicted parameter $q$. Its binary relative entropy isIt vanishes at $q=0.75$, equals approximately $2.894055$ nats at $q=0.01$, and equals $0.596495$ nats at $q=0.99$. Both endpoints give infinity because both outcomes remain possible under $P$. Predicting almost no successes is especially costly because successes actually occur three quarters of the time. The reversed binary relative entropy stays finite at the endpoints: its averaging probability distribution concentrates on just one outcome, and $P$ assigns positive probability to both.

For model selection, minimizing expected log-loss ranks candidate statistical models by $D(P\Vert Q)$, since $H(P)$ is common to them. The true probability distribution is usually unknown, so one estimates predictive performance using fresh observations or cross-validation. Fitting and assessing on the same data can reward overfitting; a small fitted Kullback-Leibler divergence alone does not establish useful prediction.

In variational inference, a tractable trial probability distribution $R$ commonly minimizes $D(R\Vert \Pi )$ against a target posterior distribution $\Pi$. Maximizing the evidence lower bound accomplishes this without evaluating the target's normalization constant. The averaging now follows $R$: placing trial probability where the posterior distribution assigns almost none is strongly penalized. The chosen direction and trial family therefore matter. ^[ref]

These comparisons do not by themselves establish causal inference. Two statistical models can reproduce the same observed probability distribution while predicting different responses to an intervention. Choosing the smaller Kullback-Leibler divergence therefore needs additional assumptions or experimental evidence before supporting a causal claim. ^[ref]

The original reference is S. Kullback and R. A. Leibler's 1951 paper ^[ref], published in The Annals of Mathematical Statistics 22(1), pages 79–86. Its title connects this comparison with sufficient statistics: the historical setting was statistical information, before today's widespread applications in prediction and variational inference.

An order-$p$ exponential family is minimal if no nonzero linear combination of its $p$ canonical sufficient statistics is almost surely constant under the base measure. Equivalently, its sufficient-statistic support lies in no proper affine hyperplane. Its cumulant function then has positive-definite Hessian matrix on the interior natural domain, because that Hessian is a covariance matrix.

A natural exponential family uses its observation vector itself as the canonical sufficient statistic. Its cumulant function is $\kappa (\theta )=\log \int h(y)e^{{\theta }^{T}y}d\nu (y)$ and its natural parameter space consists of parameters for which this integral is finite. On the interior, differentiating gives mean parameter of an exponential family $\nabla \kappa$ and covariance matrix ${\nabla }^2\kappa$.

For independent normal distribution observations with mean $\mu >0$ and variance ${\mu }^2$, put $S_1={\sum }_i{X}_i$, $S_2={\sum }_i{X}_i^2$. The Fisher-Neyman factorization theorem makes $(S_1,S_2)$ a sufficient statistic, since the likelihood is proportional to ${\mu }^{-n}\exp [-S_2/(2{\mu }^2)+S_1/\mu ]$. Its logarithmic derivative has numerator $S_2-S_1\mu -n{\mu }^2$. When $S_2>0$, this changes sign once on the positive half-line and gives the displayed global maximum-likelihood estimator. The zero sample is a probability-zero exception with unbounded likelihood as $\mu \downarrow 0$.

Put $S_1={\sum }_{i=1}^n{X}_i$, $S_2={\sum }_{i=1}^n{X}_i^2$. The joint normal distribution density can be written asThe Fisher-Neyman factorization theorem says that, for a family dominated by a common measure, a statistic $T$ is a sufficient statistic when its densities have the form $g_{\mu }(T(\mathbf{x}))h(\mathbf{x})$ with $h$ independent of the parameter. The displayed factorization therefore shows that $(S_1,S_2)$ is a two-dimensional sufficient statistic.

For maximum likelihood estimation, discard parameter-independent terms from the log-likelihood:If $S_2>0$, the quadratic equation has exactly one positive root. The numerator is positive before that root and negative after it, while the likelihood tends to zero at both ends of $\mu >0$. Hence the normal likelihood with variance equal to squared mean gives the global maximizerThis holds almost surely. The exceptional all-zero sample has $S_2=0$ and likelihood proportional to ${\mu }^{-n}$, so its supremum is approached as $\mu \downarrow 0$ and there is no positive maximizer.

A finite-dimensional exponential family has common dominating measure and densityHere $\eta$ is the natural parameter of an exponential family, and normalization determines the cumulant function of an exponential family $\kappa$. A full regular family lets $\eta$ vary over an open natural parameter domain. A curved exponential family restricts $\eta$ to a lower-dimensional submanifold, while a minimal representation removes redundant affine combinations of the statistic.

Differentiating the normalization under regularity givesConsequently $\kappa$ is convex and its Hessian is the Fisher information matrix in natural coordinates. For an independent size-$n$ sample, $S={\sum }_{i}T(Y_i)$ is a sufficient statistic: the joint density factors into ${\prod }_{i}h(y_i)$ times $\exp \{{\eta }^{\mathsf{T}}S-n\kappa (\eta )\}$, so the conditional law given $S$ loses its parameter dependence. An interior maximum-likelihood estimator solvesmatching empirical and theoretical sufficient-statistic means. Under minimality the Hessian is positive definite, so the log-likelihood is strictly concave; existence of an interior maximizer still needs the observed statistic to be in the appropriate interior convex support.

For a full open natural family, the canonical sufficient statistic is a complete statistic: if ${\mathbb{E}}_{\eta }g(S)=0$ throughout the open domain, exponential tilting about an interior point makes the Laplace transform of the integrable signed measure $g(s)P_{{\eta }_0}(ds)$ zero on a neighborhood. Transform uniqueness makes that measure zero, so $g(S)=0$ almost surely. This requires integrability under the family. It is not an automatic property of curved restrictions. The sufficient-statistic reduction, explicit information and cumulant identities make these families especially useful for exact and higher-order inference. Normal distributions, Poisson distributions and gamma distributions supply familiar examples.

A transformation model is built differently: a group acts on the sample and parameter spaces, and its probability laws satisfyIn a transitive model all laws are transforms of a fixed reference law. The location family is generated by translations, with density $f_{\mu }(y)=f_0(y-\mu )$. The positive-scale family has density $f_{\sigma }(y)={\sigma }^{-1}{f}_0(y/\sigma )$, and the location-scale family combines them asThe Jacobian factor is essential. For independent observations its power is ${\sigma }^{-n}$. The base density need not have moments, and a transformation model need not have fixed support.

An equivariant estimator respects the parameter action. With a well-defined equivariant fit $T(y)$ valued in the transformation group, the normalized sampleis a maximal invariant. Equivariance gives $a(gy)=a(y)$; if normalized samples agree, $y^{\prime }$ is obtained from $y$ by $T(y^{\prime })T(y{)}^{-1}$. In a transitive transformation model its law is parameter-free, so it is an ancillary statistic. For location and positive scale, centered standardized residuals provide this construction on nondegenerate samples. If the parameter action has a nontrivial stabilizer, normalization must retain its quotient action rather than pretending that a unique group element is determined.

This geometry motivates conditioning on an ancillary and using the P-star approximation for the fitted parameter. Ancillarity means parameter-free distribution; it does not by itself assert independence from the estimator. In a normal location model with known scale, the sample mean and centered residuals are independent by their joint Gaussian law and zero covariance. In an exponential scale model, the sample sum and its normalized proportions are independent, again an additional model property.

The two classes overlap but are distinct. A normal location-scale family is both an exponential family and a transformation model; an exponential mean family is both a one-dimensional natural exponential family and a scale model. A general Cauchy distribution location family is a transformation model without a fixed finite-dimensional regular exponential-family sufficient-statistic reduction. Exponential-family structure emphasizes sufficient statistics and natural-parameter convexity; transformation structure emphasizes equivariance, group orbits and ancillary reduction.

Consider a regular $d$-parameter statistical model for which the data can locally be expressed as $(v,a)$, where $v=\hat{\theta }$ is the maximum-likelihood estimator and $a$ is a suitable exact or higher-order ancillary statistic. Write the log-likelihood as $\ell (\theta ;v,a)$, and letbe its fitted observed information. The P-star approximation to the conditional sampling density of $v$, given $a$, isThe normalizing factor is independent of $v$ and is chosen so the density integrates to one over the feasible estimator coordinates. Its leading Gaussian value is $(2\pi {)}^{-d/2}$, but exact normalization can change it. Both the fitted likelihood and the fitted information vary with $v$; the observed dataset is not held fixed while integrating over this sampling coordinate.

To see the structure, expand the likelihood difference for $v$ close to $\theta$. Its leading term is minus one half of the information-weighted quadratic displacement, while $|j(v){|}^{1/2}$ supplies the local density scale. This recovers the first-order multivariate normal distribution of an efficient maximum-likelihood estimator. The formula is invariant under smooth one-to-one parameter changes: at a likelihood maximum the score vanishes, so the Hessian transforms as a quadratic form. Its square-root determinant supplies exactly the Jacobian determinant required for the estimator density to transform.

There is a direct derivation in a full regular exponential family. Let $S$ be the sufficient statistic for an independent sample, with likelihoodThe maximum-likelihood equation is $s=n\nabla \kappa (v)$, and its derivative is $j(v)=n{\kappa }^{\prime \prime }(v)$. Applying a multivariate saddlepoint density approximation to $S$ givesChanging variables from $s$ to $v$ multiplies by $|ds/dv|=|j(v)|$, producing the displayed P-star form. In more general models, ancillary conditioning supplies the relevant local sample coordinates; an ancillary is not an arbitrary extra statistic.

For an illustrative exact case, take an independent sample from an exponential distribution with mean $\theta$, so $v=\bar{Y}$. The fitted observed information is $j(v)=n/v^2$, andThus P-star has density shape $v^{n-1}{\theta }^{-n}{e}^{-nv/\theta }$. Normalizing giveswhich is exactly the gamma distribution sampling density of the mean. Here sample ratios provide ancillary coordinates and are independent of the mean.

In suitable regular models the unnormalized leading construction has relative error of order $n^{-1}$, and higher-order ancillary constructions with normalization can attain relative order $n^{-3/2}$. Such accuracy needs the stated smoothness and ancillary hypotheses; it is not a universal consequence of asymptotic normality. The exact scale-model example is likewise a special property, not a general identity. The formula is useful for refined conditional inference, score distributions, and nuisance-likelihood adjustments, including modified profile likelihood. It describes a sampling density of the estimator, rather than a posterior density of the parameter.

For a dominated family of sample distributions, a statistic $T$ is a sufficient statistic precisely when the probability mass function or density factors aswhere $h$ does not depend on the parameter. This is the Fisher-Neyman factorization theorem.

Here is the discrete proof in both directions. Suppose the factorization holds, and write $H(t)={\sum }_{x:T(x)=t}h(x)$. Then ${\mathbb{P}}_{\theta }(T=t)=g_{\theta }(t)H(t)$. Whenever this probability is positive, $H(t)$ is finite and positive andis independent of $\theta$. Thus the conditional distribution of the whole sample given $T$ is parameter-free, which is the definition of a sufficient statistic.

Conversely, if $T$ is sufficient, let $q_t(x)$ denote this common conditional mass function on each possible fiber. ThenTake $g_{\theta }(t)={\mathbb{P}}_{\theta }(T=t)$ and $h(x)=q_{T(x)}(x)$ to obtain the factorization. Fibers impossible for every parameter can be assigned $h=0$; conditional probabilities are required only for fibers with positive probability.

For the independent Poisson sample the joint mass function isAll parameter dependence is throughso this is a one-dimensional sufficient statistic. The sample mean contains the same information, since $n$ is known.

The P-star approximation describes the sampling density of a regular maximum-likelihood estimator in sample coordinates consisting of the fitted parameter $\hat{\theta }$ and a suitable ancillary statistic $a$. For a $d$-dimensional parameter, write $\ell (\theta ;\hat{\theta },a)$ for the log-likelihood expressed in these coordinates, and $j(\hat{\theta };\hat{\theta },a)$ for the fitted observed information. The approximation isThe determinant factor supplies the local volume scale, while the exponential retains the full likelihood ratio, rather than replacing it by a quadratic. The normalizing constant is chosen by integrating over the fitted-parameter coordinates; its leading regular large-sample value is $(2\pi {)}^{-d/2}$. These ingredients arise from a statistical saddlepoint approximation. Their use requires an interior, locally unique fit, nonsingular information and suitable smoothness; an ancillary statistic specifies the conditioning surface when the fit alone does not exhaust the relevant sample coordinates. In a regular model the unnormalized leading expression has relative error of order $n^{-1}$ locally; normalization and conditioning matter for refined inference. It is not a universal exact density. Under a smooth one-to-one parameter transformation, the Hessian at the fit transforms without a score term, so its determinant supplies precisely the density Jacobian determinant. This proves the invariance of the construction under one-to-one changes of statistical parameters.

Here put $S={\sum }_i{X}_i$ and $T={\sum }_i{Y}_i$. The log-likelihood, apart from a data-only constant, isThe two score equations giveThe sample sums form a sufficient statistic, and there is no need to retain an additional ancillary in calculating their joint density. Inverting the fitted coordinates gives $S=n/\hat{\lambda }$ and $T=n/(\hat{\psi }\hat{\lambda })$. With parameter order $(\psi ,\lambda )$, the fitted observed information isAlso,Thus the required joint P-star approximation isTo integrate out $\hat{\lambda }$, substitute $u=b/\hat{\lambda }$ inTaking $b=n\lambda (1+\psi /\hat{\psi })$ and rearranging the powers yieldsThe beta function identity ${\int }_0^{\infty }{z}^{n-1}(1+z{)}^{-2n}dz=\Gamma (n{)}^2/\Gamma (2n)$ therefore givesBoth normalized approximations are actually exact. To check the joint assertion independently, $S$ and $T$ have independent gamma distributions with shape $n$ and rates $\lambda$ and $\psi \lambda$. Their joint density is ${\psi }^n{\lambda }^{2n}{S}^{n-1}{T}^{n-1}{e}^{-\lambda S-\psi \lambda T}/\Gamma (n{)}^2$. The inverse-coordinate Jacobian determinant iswhich reproduces the normalized joint expression. This is the P-star density for two exponential rates; its marginal gives the stated F-distribution. Stirling's approximation gives $c_n=(2\pi {)}^{-1}(1-1/(6n)+O(n^{-2}))$, so using only the leading normalizer retains the exact shape but not its exact total mass.

The original PDF's displayed density constant is missing a square on $\Gamma (n)$. Its printed expression integrates to $\Gamma (n)$, rather than one in general. The normalized density above is the one consistent with the stated F-distribution.