BayesFlow Monte Carlo (contrib)

Monte Carlo integration and helpers.


Monte Carlo integration refers to the practice of estimating an expectation with a sample mean. For example, given random variable Z in \(R^k\) with density p, the expectation of function f can be approximated like:

$$E_p[f(Z)] = \int f(z) p(z) dz$$
$$ ~ S_n := n^{-1} \sum_{i=1}^n f(z_i), z_i\ iid\ samples\ from\ p.$$

If \(E_p[|f(Z)|] < infinity\), then \(S_n\) --> \(E_p[f(Z)]\) by the strong law of large numbers. If \(E_p[f(Z)^2] < infinity\), then \(S_n\) is asymptotically normal with variance \(Var[f(Z)] / n\).

Practitioners of Bayesian statistics often find themselves wanting to estimate \(E_p[f(Z)]\) when the distribution p is known only up to a constant. For example, the joint distribution p(z, x) may be known, but the evidence \(p(x) = \int p(z, x) dz\) may be intractable. In that case, a parameterized distribution family \(q_\lambda(z)\) may be chosen, and the optimal \(\lambda\) is the one minimizing the KL divergence between \(q_\lambda(z)\) and \(p(z | x)\). We only know p(z, x), but that is sufficient to find \(\lambda\).

Log-space evaluation and subtracting the maximum

Care must be taken when the random variable lives in a high dimensional space. For example, the naive importance sample estimate \(E_q[f(Z) p(Z) / q(Z)]\) involves the ratio of two terms \(p(Z) / q(Z)\), each of which must have tails dropping off faster than \(O(|z|^{-(k + 1)})\) in order to have finite integral. This ratio would often be zero or infinity up to numerical precision.

For that reason, we write

$$Log E_q[ f(Z) p(Z) / q(Z) ]$$
$$ = Log E_q[ \exp\{Log[f(Z)] + Log[p(Z)] - Log[q(Z)] - C\} ] + C,$$ where
$$C := Max[ Log[f(Z)] + Log[p(Z)] - Log[q(Z)] ].$$

The maximum value of the exponentiated term will be 0.0, and the expectation can be evaluated in a stable manner.