# Proof of posterior consistency

Given the model $x_1,...,x_n$ ~ $Poi(\theta)$ (iid), and the prior $\theta$~$Gamma(a,b)$. How do we prove the consistency of posterior distribution? Thanks in advance.

Modification: The definition of posterior consistency given on my lecture slides is:

“given the data $x_1,...,x_n$ iid and the model f(x;$\theta$), if we have posterior consistency at $\theta_0$, then $\forall \epsilon$, $\lim_{n\rightarrow +\infty}\pi(|\theta-\theta_0|>\epsilon|X)$=0.” $\theta_0$ is not specified but I think it is the true value of $\theta$.

• Could you explain what you mean for a distribution to be consistent? – whuber Dec 22 '17 at 19:34
• I have added this in the original question. – Maxxx. Dec 22 '17 at 20:06
• Thank you. That was important, because consistency is not a property of any distribution: it's a property of the estimator and the model. (Presumably, "$\pi$" means "probability.") As far as your question goes, could you please indicate what attempts you have made towards a solution? For instance, have you worked out what the posterior distribution actually is in terms of $a$, $b$, and $X$? – whuber Dec 22 '17 at 20:48

Writing $X^{(n)}=(X_1,\dots,X_n)$, Bayes theorem tells us that $$\Theta\mid X^{(n)}\sim\mathrm{Ga}\!\left(a+\sum_{i=1}^n X_i,\,b+n\right).$$ Define $$M=\mathrm{E}[\Theta\mid X^{(n)}]=\frac{a+\sum_{i=1}^n X_i}{b+n}$$ and $$V=\mathrm{Var}[\Theta\mid X^{(n)}]=\frac{a+\sum_{i=1}^n X_i}{(b+n)^2}.$$ For any $\epsilon>0$, using the triangle inequality we have $$\epsilon \leq |\Theta-\theta_0| = |\Theta-M+M-\theta_0|\leq |\Theta-M|+|M-\theta_0|.$$ Hence, monotonicity and Chebyshev's (Чебышёв) inequality give us $$\Pr(|\Theta-\theta_0|\geq\epsilon\mid X^{(n)}) \leq \frac{4V}{\epsilon^2} + \Pr(|M-\theta_0|\geq\epsilon/2\mid X^{(n)}).$$ Let $\mu_0$ be a $\mathrm{Poi}(\theta_0)$ distribution and note that $V\to 0$ a.s. $[\mu_0]$, and $M\to \theta_0$ a.s. $[\mu_0]$, as $n\to\infty$. This is essentially what you need to see that this posterior is "simulation consistent" (that is, if you're simulating iid $X_i$'s from a $\mathrm{Poi}(\theta_0)$ distribution, then your posterior distribution will concentrate all its mass at $\theta_0$ as your sample size becomes arbitrarily large). To learn how to write a precise proof, please study Schervish (Theory of Statistics), section 7.4.