Soft Execution

# Soft Execution

Note

TLDR: For inference problems which are continuous or high dimensional, use soft predicates to use more efficient inference routines.

==ₛ
>ₛ
>=
<=ₛ
<ₛ

Soft predicates are supported (and required) by inference algorithms: SSMH, HMCFAST, NUTS, Replica.

If the values x and y are not standard numeric types you will need to define a notion of distance (even if they are, you may want to wrap them and define a distance for this type). Override Omega.d for the relevant types

Omega.d(x, y)

## Relaxation

In Omega you condition on predicates. A predicate is any function whose domain is Boolean. These are sometimes called indicator functions or characteristic functions. In particular, in Omega we condition on Bool valued random variables:

x = normal(0.0, 1.0)
y = x == 1.0
rand(y)

From this perspective, conditioning means to solve a constraint. It can be difficult to solve these constraints exactly, and so Omega supports softened constraints to make inference more tractable.

To soften predicates, use soft counterparts to primitive predicates. Suppose we construct a random variable of the form x == y. In the soft version we would write x ==ₛ y (or softeq(x, y)).

julia> x = normal(0.0, 1.0)
julia> y = x ==ₛ 1.0
julia> rand(y)
ϵ:-47439.72956833765
Note

In the Julia REPL and most IDEs ==ₛ is constructed by typing ==\_s [tab].

Softened predicates return values in unit interval [0, 1] as opposed to a simply true or false. Intuitively, 1 corresponds to true, and a high value (such as 0.999) corresonds to "nearly true". In practice, we encode this number in log scale [-Inf, 0] for numerical reasons. Mathematicallty, soft predicates they have the form:

$k_\alpha(\rho(x, y))$

If $\rho(x, y)$ denotes a notion of distance between $x$ and $y$. Distances are determined by the method Omega.d

Distance between two values

source

Rather than output values of type Float64, soft predicate output values of type SoftBool.

Soft Boolean. Value in [0, 1]

source

## Distances and Kernels

Omega has a number of built-in kernels:

(Log) Squared exponential kernel α = 1/2l^2, higher α is lower temperature

source

Real+ -> [0, 1]

source
Missing docstring.

Missing docstring for kpareto. Check Documenter's build log for details.

By default, the squared exponential kernel kse is used with a default temperature parameter. The method withkernel can be used to choose which kernel is being applied within the context of a soft boolean operator.

Temporarily set global kernel

source