Thanks for feedbach, this is much appreciated! I fixed the obvious bug in the most recent version. Here are some comments: - The Ai in the Law of Total Probab

Thanks for feedbach, this is much appreciated! I fixed the obvious bug in the most recent version. Here are some comments: - The Ai in the Law of Total Probability indeed have to be disjoint. I indicated this by using a squared union symbol, though have not introduced this notation elsewhere. There are several implicit notation assumptions throughout the cookbook. For consistency reasons, I'll either address all of them or none. - I demoted the wrong equivalence to an implication in the sum of variances. I purposefully did not write "if Cov[X_i, x_j] = 0" because that is obvious from the statement above. - I simply removed the unnecessary finite variance restriction from the LLN discussion. With the notation E[X_i] = mu I mean to imply E[X_i] < infinity, I hope this is clearer now. - The Stochastic Processes is section has a very narrow focus. Indeed, it would benefit from further extesion. At this point, I unfortunately do not have the cycles to add new content myself, but feel free to do so by submitting a pull request. - Similarly, if you find a consistent way of integration Kolmogorov's extension theorem, I'd be happy to merge a pull request. However, note that I have not yet introduced the notion of a measure in the cookbook, which appears to be necessary ingredient of the theorem argumentation.