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}
|
