# How do we define the kernel to calculate the acceptance ratio for Metropolis-Hastings Markov Chain Monte Carlo?

I am having a lot of difficulty understanding how to apply the algorithm to a real scenario.

The part that confuses me is that we are looking for a target distribution (the real distribution of our parameters), but we somehow know a kernel distribution that is proportional to the target distribution, since we use it to compute the acceptance ratio. How do we find/define that kernel distribution?

I would like to understand this through a concrete example. Let's say I have real-life data recorded from a system that is modelled by a PDE with 5 parameters. My goal is to create probability distributions of each of those parameters. I understand the idea of the MH-MCMC algorithm, but when it comes to computing the acceptance ratio (which requires the kernel distribution), I don't understand how we define it.

I might be missing something obvious due to my lack of statistics background.

• you need to know the target density up to a constant, but the kernel is usually understood as the density of the proposal distribution which is completely arbitrary. Jan 24, 2021 at 21:23
• @Xi'an What do you mean by “up to a constant”? Also, isn’t the kernel a distribution that is proportional to the target distribution, and not the proposal distribution?
Jan 24, 2021 at 23:34
• To understand what "up to a constant" means, consider a target f(x)=g(x)C, and you can only evaluate g(x) but not f(x) because you don't know C. Think of C as the constant that normalizes g(x) to f(x). You only need to evaluate g(x) because in the acceptance ratio of MH, you actually evaluate f(x), but it simplifies g(y)c/[g(x)c=]g(y)/g(x).
– user
Jan 25, 2021 at 7:25
• @user228809 Oh, evaluating g(y)/g(x) is the same as f(y)/f(x), I see! Now I’m still asking my initial question, how can we find a g function proportional to f? Considering my example above.
To compute the Metropolis-Hastings ratio for a target density $$f(\cdot)$$ with kernel proposal $$k(\cdot|\cdot)$$ one need compute $$\dfrac{f(y)k(x|y)}{f(x)k(y|x)}$$ for an arbitrary pair $$(x,y)$$ or find unbiased estimators $$\hat f(y)$$ and $$\hat f(x)$$ in the pseudo-marginal version of Andrieu & Roberts (2009).