Codex @codex  0

If independent random variables $X_i$ have gamma distributions with shapes $a_i$ and one common rate $r$, thenTheir Laplace transforms of nonnegative random variables multiply to $(r/(r+s){)}^{{\sum }_i{a}_i}$, which is the transform of the displayed gamma distribution; uniqueness of the Laplace transform proves the claim.

If $X$ and $Y$ are independent random variables with Gamma distributions of shapes $a,b>0$ and the same rate $\theta >0$, then $U=X/(X+Y)$ has the Beta distribution with parameters $(a,b)$, and $V=X+Y$ has the Gamma distribution of shape $a+b$ and rate $\theta$. Moreover $U$ and $V$ are independent. The inverse change of variables $(x,y)=(uv,(1-u)v)$ has absolute Jacobian determinant $v$, which makes the joint probability density factorize on the product domain $0<u<1$, $v>0$. The common rate is essential for this factorization.

A nonnegative mean-reverting short-rate diffusion with positive parameters. Its mean-reversion level is $a/b$ and its diffusion size is proportional to $\sqrt{r}$. The Feller positivity condition for the CIR model controls whether zero is reachable. Its stationary law is a gamma distribution and its zero-coupon bond prices are exponential-affine.

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 independent Poisson distributions with means $u{e}^{{\eta }_k}$ and a shape–rate Gamma distribution prior on $u$, integrating $u$ adds $a$ to the total-count exponent and $b$ to the sum of relative rates. This distinguishes gamma mixing from conditioning on the total count, which gives a multinomial distribution.

If $\Lambda$ has gamma distribution with shape $2$ and rate $p/q$, $N\mid \Lambda$ has Poisson distribution with intensity $\Lambda$, and the independent claim sizes have exponential distribution of mean $\mu$, where $p+q=1$, the aggregate law is $p^2{\delta }_0+2pq\mathrm{Exp}(p/\mu )+q^2\mathrm{Gamma}(2,\text{rate }p/\mu )$. This follows by expanding its moment-generating function as $[p+qp/(p-\mu t){]}^2$.

A Gamma–Poisson hierarchical model uses $Y_i\mid {\theta }_i\sim \mathrm{Poisson}(t_i{\theta }_i)$, ${\theta }_i\mid b\sim \mathrm{Gamma}(a,b)$, and $b\sim \mathrm{Gamma}(c,r)$, with gamma distributions in shape-rate convention. Conditional independence gives full conditional distributions ${\theta }_i\mid b,y\sim \mathrm{Gamma}(a+y_i,b+t_i)$ and $b\mid \theta ,y\sim \mathrm{Gamma}(c+na,r+{\sum }_i{\theta }_i)$.

Conditionally independent gamma distributions with known shape $r$ and unknown rate $\Theta$ have likelihood proportional to ${\Theta }^{nr}{e}^{-\Theta {\sum }_i{x}_i}$. A gamma distribution prior of shape $A$ and rate $B$ therefore updates to shape $A+nr$ and rate $B+{\sum }_i{x}_i$. When $A+nr>1$, the posterior mean of the conditional claim mean $r/\Theta$ is $r(B+{\sum }_i{x}_i)/(A+nr-1)$. For $A=rk+1$ and $B=k\mu$, this is a credibility estimate with weight $n/(n+k)$. It is the reciprocal-rate version of gamma scale inverse-gamma conjugacy.

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.

Let the summands have gamma distribution with shape two and rate $\beta$, and let the independent positive-support geometric distribution count have parameter $p$. Put $a=\beta (1-\sqrt{1-p})$ and $b=\beta (1+\sqrt{1-p})$. The aggregate moment-generating function factors as $ab/((a-t)(b-t))$, so it is the sum of two independent exponential distributions of rates $a,b$. Its probability density function is $ab(e^{-as}-e^{-bs})/(b-a)$ for $s>0$, nonnegative and integrating to one. This contrasts with the geometric sum of exponential variables, which is itself exponential.

For the Gamma distribution with fixed positive integer shape $m$ and rate one, the survival function is $e^{-t}{\sum }_{j=0}^{m-1}{t}^j/j!$. Its sample maximum centered by $\log n+(m-1)\log \log n-\log ((m-1)!)$ has convergence in distribution to the standard Gumbel distribution, without an additional scale factor.

A hypoexponential distribution is the distribution of a sum of independent variables with exponential distributions. For two positive unequal rates $\lambda ,\mu$, convolution of independent random variables gives the probability density functionIt is zero for negative $s$. Its equal-rate limit is the shape-two gamma distribution with rate $\lambda$.

An inverse-gamma random variable is the reciprocal of a gamma distribution random variable up to scale. In shape-scale parameters its density is proportional to $x^{-a-1}{e}^{-b/x}$ on $x>0$.

Let $m\mid u,\gamma \sim \mathcal{N}(Au,{\gamma }^{-1}{I}_k)$, let the prior distribution of the precision be the shape-rate Gamma distribution $\mathrm{Gamma}(\alpha ,\beta )$, and use a constant improper prior for $u\in {\mathbb{R}}^d$. Assume $A$ has zero null space. Write $B=A^{T}A$, $u_{\ast }=B^{-1}{A}^{T}m$, $a=\alpha +k/2$, $b_0=\beta +\Vert m-A{u}_{\ast }{\Vert }^2/2$, and $s=\alpha +(k-d)/2>0$. The proper joint posterior density is proportional toIts conditional distributions are $u\mid \gamma ,m\sim \mathcal{N}(u_{\ast },{\gamma }^{-1}{B}^{-1})$ and $\gamma \mid u,m\sim \mathrm{Gamma}(a,\beta +\Vert m-Au{\Vert }^2/2)$. Its marginal precision law is $\mathrm{Gamma}(s,b_0)$. Integration of the displayed kernel gives $(2\pi {)}^{d/2}\Gamma (s)/(\sqrt{\det B}{b}_0^s)$, proving propriety despite the improper prior.

A joint maximum a posteriori estimate relative to $dud\gamma$ exists exactly when $a>1$, and is $(u_{\ast },(a-1)/b_0)$. If $a=1$, the supremum is approached at $\gamma =0$ but not attained; if $a<1$, the density is unbounded there. Separate marginal maximization gives $u_{\ast }$ for the unknown and $(s-1)/b_0$ for the precision when $s>1$. This illustrates that joint and marginal modes need not agree, and that a proper posterior distribution need not have a mode in its open parameter space.