Machine Learning, Aritificial Intelligence, Data Science, Programming, Deep Learning.
for the expectation, we choose That is we want q* ∝ |f(x)| p(x), i.e. we want q* to reflect the high probability areas of x that have high |f(x)| value (proof). The low variance allows us to draw fewer samples. In a Bayesian model, p is easy to compute. But how can we extend it to other models