On one hand, mathematically speaking, the problem of sampling a finite state system when the probabilities of each state are known is a trivial one, as you kn

On one hand, mathematically speaking, the problem of sampling a finite state system when the probabilities of each state are known is a trivial one, as you know very well since you have shown that method in your posts (computing cumulative probabilities ...) On the other side, your blog is about computation and math, and in the realm of computation the complexity of algorithms is a key factor. Mixing time and burning time of markov chains are also computationally important. The curse of dimensionality is a cause of Monte Carlo methods to be used instead of other numerical methods in high dimensional problems. You should communicate to your audience that the advances in stochastic sampling and its application to science has revolutionize the field not because a breathrough in maths but because great key insights that allow us to explore multidimensional problems effectively with the help of computers. Another important factor is that MCMC is used extensively in Bayesian statistics, but to explain that main application of MCMC in your blog it is necessary to introduce concepts like Bayer's theorem, likelihood, posterior density function, and prior probabilities. There are some blogs in which all those concepts are illustrated and code in R or python is provided. Perhaps MCMC requires more than one post to be adequately explained. To sum up, I am a follower of your posts and I appreciate the effort you take to show the main ingredients and the python code. My only desire is for you to continue improving the content of your blog for us to follow and enjoy. Merry Christmas to you and your family from a follower in Spain.