I'm working on a problem involving Bayesian updating with a Beta prior, but the data I observe comes from a slightly complex source.

Let $X \sim \text{Bernoulli}(p)$ and $Y \sim \text{Bernoulli}(q)$, where $q$ is known. I don't directly observe $X$. Instead, I observe $Z = XY$. I would like to estimate $p$.

Here are some intuition based on some preliminary calculations:

  • If $Z = 1$, I believe the update on the prior $\text{Beta}(\alpha, \beta)$ should be $\text{Beta}(\alpha+1,\beta)$ since $X$ definitely equals 1.

  • If $Z = 0$, I deduced that the update should be $\text{Beta}(\alpha+P(X=1|Z=0),\beta+1−P(X=1|Z=0))$.

Can someone provide a detailed proof or justification for these updates? Specifically, I'm interested in the logic behind the fractional update when observing $Z = 0$ and its validity within the Bayesian framework.


1 Answer 1


Your intuition here is not quite accurate and the situation is a bit more complicated than the way you are treating it. Nevertheless, since the parameter $q$ is known, this problem is a simple variation of the standard beta-binomial model used widely in Bayesian analysis. Since $X \sim \text{Bernoulli}(p)$ and $Y \sim \text{Bernoulli}(q)$ you have:

$$Z = XY \sim \text{Bernoulli}(pq).$$

If we observe IID data $Z_1,...,Z_n$ from this distribution then we obtain the sufficient statistic:

$$\dot{Z}_n \equiv \sum_{i=1}^n Z_i \sim \text{Bin}(n, pq).$$

Taking the prior $p \sim \text{Beta}(\alpha, \beta)$ then gives the posterior kernel:

$$\begin{align} \pi(p|\mathbf{z}_n, q) &\propto \text{Bin}(\dot{z}_n | n, pq) \cdot \text{Beta}(p|\alpha, \beta) \\[6pt] &\propto (pq)^{\dot{z}_n} (1-pq)^{n-\dot{z}_n} p^{\alpha-1} (1-p)^{\beta-1}. \\[6pt] \end{align}$$

Consequently, the posterior density function is:

$$\pi(p|\mathbf{z}_n, q) = \frac{(pq)^{\dot{z}_n} (1-pq)^{n-\dot{z}_n} p^{\alpha-1} (1-p)^{\beta-1}}{\int_0^1 (pq)^{\dot{z}_n} (1-pq)^{n-\dot{z}_n} p^{\alpha-1} (1-p)^{\beta-1} \ dp}.$$

The scaling constant determined by the integral does not have a closed-form solution so you would need to evaluate it numerically. Alternatively you could simulate from the posterior distribution using MCMC methods or importance sampling methods.


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