Consider the following (traditional) program: x = raw_input() x *= 2 assert x >= 10 Now we can ask the question: "Assuming the assertion passed, what ca

Consider the following (traditional) program: x = raw_input() x *= 2 assert x >= 10 Now we can ask the question: "Assuming the assertion passed, what can we say about x?" In this simple example, we know that x >= 5, but in general the possible values of x may be much more complicated. This is the kind of question probablistic programming is designed to answer, except instead of being an arbitrary, unknown value, x is specified as some distribution (say Gaussian). Then, the question becomes, "What is the posterior distribution of x, assuming all of the assertions pass?" In other words, figure out how likely different choices of x were, taking into account both the prior (i.e., that x is Gaussian) and the new information from the assertions. For a simple example of why this is useful, suppose we have a program that generates random images (this is our prior). We also have some real photographs. We can assert that the random image generator should have a pretty good change of generating the real photographs. Then, the probablistic program "execution" will try and compute a new generator that creates more realistic images.