1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
|
# BayesFlow Monte Carlo (contrib)
[TOC]
Monte Carlo integration and helpers.
## Background
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.
## Ops
* @{tf.contrib.bayesflow.monte_carlo.expectation}
* @{tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler}
* @{tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler_logspace}
|
