/TensorFlow Python

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.


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