I've seen the following claim in the context of likelihood ratio tests but I'm having a hard time verifying the result.
Suppose I have an iid sample $x_1, \ldots, x_n$ drawn from a distribution with parameter $\theta \in \Theta$. The likelihood ratio for testing $H_0: \theta = \theta_0$ versus some alternative $\theta$ is $$ \Lambda_n(\theta) = \frac{L(\theta \,|\, x_1, \ldots, x_n)}{L(\theta_0 \,|\, x_1, \ldots, x_n)} $$ is a martingale under $H_0$ with respect to $\{\mathcal{F}_n: n \geq 1\}$, the filtration generation by $x_1, \ldots, x_n$.
The claim is that the mixture of this likelihood ratio with respect to a Normal prior $H$ on $\theta$ is also a martingale, i.e. letting $$ M_n = \int_{\Theta} \Lambda_n(\theta) \, dH(\theta)$$ then $\{M_n: n \geq 1\}$ is also a martingale with respect to $\{\mathcal{F}_n: n \geq 1\}$.
I've tried writing out the likelihood explicitly to show $E(M_n \,|\, \mathcal{F}_{n-1}) = M_{n-1}$, but I'm not sure how to proceed further past: $$ E \left[ \int_{\Theta} \prod_{i=1}^{n-1} \frac{L(\theta \,|\, x_1, \ldots, x_{n-1})}{L(\theta_0 \,|\, x_1, \ldots, x_{n-1})} \cdot \frac{L(\theta \,|\, x_n)}{L(\theta \,|\, x_n)} dH(\theta) \; \Big| \; \mathcal{F}_{n-1} \right] $$