Estimating the posterior of a parameter vector which is only observed after linear transformation

Question

I have a known matrix M (square of dimension D) and a parameter vector v (of length D) which is unknown to me and whose posterior distribution I am trying to estimate. My prior on v is that each of its components is standard normal.

For example, let's say that M looks like this:

[[1, -1, -1]
 [1,  0,  2]
 [1,  1, -1]]

My observed data is a of the form R * M * v where R is a "reduction" (non-square) matrix that effectively only allows me to observe some of the components of M * v. For example, R might look like this (keeping the first and second components of M * v):

[[1,  0,  0]
 [0,  1,  0]]

What's the right way to set up a problem like this in STAN?