> Require initial knowledge of many probabilities. This is not a really big issue. You can always use uninformative priors, which is simply the Bayesian way of

> Require initial knowledge of many probabilities. This is not a really big issue. You can always use uninformative priors, which is simply the Bayesian way of saying "I don't know" what the probability distribution looks like upfront. Then the data can help you infer the right shape of probabilities. > Can become computationally intractable. This is a bummer, but modern methods like variational inference and better sampling techniques are making Bayesian analysis on large-scale data and complex models tractable. I linked a video the article that I highly recommend watching. Variational Bayes and Beyond: Bayesian Inference for Big Data https://www.youtube.com/watch?v=DYRK0-_K2UU https://www.youtube.com/watch?v=DYRK0-_K2UU