# Approximating the posterior

I would like to determine the MAP estimate $\hat\theta$ for a posterior distribution $P(\theta|x)$, where $x$ is data. The exact posterior is difficult to evaluate but I can approximate by $Q(\theta)$ and show that

$$\left|\log\left[\frac{P(\theta|x)}{Q(\theta)}\right]\right|<\epsilon$$

for some small number $\epsilon$. Intuitively, this should translate to $\arg\max_\theta P(\theta|x)$ and $\arg\max_\theta Q(\theta)$ being close. But is there a formal result at the level of $P(\theta|x)$ and $Q(\theta)$ or do I need to prove that the gradients are close?

Unless the function is Lipschitz, there is no connection between the proximity of the functions and the proximity of the modes: take the two functions $$P(\theta|x)=\cases{1+\epsilon &\text{if }0<x<\epsilon\\1&\text{if }\epsilon<x<1}$$ and $$Q(\theta)=\cases{1+\epsilon &\text{if }1-\epsilon<x<1\\1&\text{otherwise}}$$ Those functions only differ by $\epsilon$ at most, but the modes are at opposite ends of $(0,1)$.