I am writing a load test for a web application. I have a few month's worth of access logs that tell me relative hit rates of various URLs. However, the access logs do not contain enough information to tell me where a user is likely to click next. In other words, the access logs cannot help me deduce whether, given that a user is currently on page A, they are more likely to go to page B or page C.
I know which page transitions are possible, i.e. you can navigate directly from page A to page B, but not from page A to page D. I can use that information to construct a graph. If I weight the edges of the graph subject to certain constraints (e.g. the sum of the weights of all edges out of a node must equal 1), I will have a Markov chain. I would like to use the Markov chain to model variability in user behavior, but it is not obvious how to assign the weights.
If I know the relative hit rates that I want to achieve, and I know the Markov's chain's structure but not its weights, what techniques are available for assigning the weights?