bayesAB (version 1.1.3)

banditize: Create a multi-armed Bayesian bandit object.

Description

Fit a multi-armed bandit object based on a bayesTest which can serve recommendations and adapt to new data.

Usage

banditize(bT, param, higher_is_better = TRUE)

Arguments

bT

a bayesTest object

param

which model parameter (posterior) to evaluate; defaults to first param

higher_is_better

is a higher value of `param` equivalent to a better choice?

Value

A bayesBandit object.

Details

banditize is an 'object-oriented' implementation of multi-armed bandits in bayesAB. It is useful in conjunction with a Shiny app or Plumber deployment. The object itself is mutable and can adapt/learn from new data without having to re-assign the variable.

Comes with 5 methods:

• serveRecipe(): serves a recipe to show your user based on samples from both posteriors.

• setResults(results): set results for one or more recipes for one or more instances of feedback. Used to update bandit.

• getBayesTest(): returns most updated bayesTest object.

• getOriginalTest(): returns original bayesTest object without any updates.

• getUpdates(): returns a summarized version of all updates this bandit has processed.

Examples

# NOT RUN {
A_binom <- rbinom(100, 1, .5)
B_binom <- rbinom(100, 1, .6)

AB1 <- bayesTest(A_binom, B_binom, priors = c('alpha' = 1, 'beta' = 1), distribution = 'bernoulli')

binomialBandit <- banditize(AB1)
binomialBandit$serveRecipe()
binomialBandit$setResults(list('A' = c(1, 0, 1, 0, 0), 'B' = c(0, 0, 0, 0, 1)))

# }