This package provides an implementation of the cross entropy method for optimizing multivariate time series distributions.
Suppose we have a timeseries `X = {x₁, ..., xₙ}`

where each `xᵢ`

is a vector of dimension `m`

. This package provides optimization for two different scenarios:

- The time series is sampled IID from a single distribution
`p`

:`xᵢ ~ p(x)`

. In this case, the distribution is represented as a`Dict{Symbol, Tuple{Sampleable, Int64}}`

. The dictionary will contain`m`

symbols, one for each variable in the series. The`Sampleable`

object represents`p`

and the integer is the length of the timeseries (`N`

) - The time series is sampled from a different distribution at each timestep
`pᵢ`

:`xᵢ ~ pᵢ(x)`

. In this case, the distribution is also represented as a`Dict{Symbol, Tuple{Sampleable, Int64}}`

.

Note: The `Sampleable`

objects must support the `Distributions.jl`

function `logpdf`

and `fit`

.

See the `examples/`

folder for an example use case.
The main function is `cross_entropy_method`

and has the following parameters:

`loss::Function`

- The loss function. No default.`d_in`

- The starting sampling distribution. No default.`max_iter`

- Maximum number of iterations, No default.`N`

- The population size. Default:`100`

`elite_thresh`

- The threshold below which a sample will be considered elite. To have a fixed number of elite samples set this to`-Inf`

and use the`min_elite_samples`

parameter. Default:`-0.99`

`min_elite_samples`

- The minimum number of elite samples. Default:`Int64(floor(0.1*N))`

`max_elite_samples`

- The maximum number of allowed elite samples. Default:`typemax(Int64)`

`weight_fn`

- A function that specifies the weight of each sample. Use the likelihood ratio when trying to perform importance sampling. Default`(d,x) -> 1`

`rng::AbstractRNG`

- The random number generator used. Default:`Random.GLOBAL_RNG`

`verbose`

- Whether or not to print progress. Default:`false`

`show_progress`

- Whether or not to show the progress meter. Default:`false`

`batched`

- Indicates batched loss evaluation (loss function must return an array containing loss values for each sample). Default:`false`

`add_entropy`

- A function that transforms the sampling distribution after fitting. Use it to enforce a maximum level of entropy if converging too quickly. Default:`(x)->x`

Maintained by Anthony Corso (acorso@stanford.edu)