simOAB: Bayesian One-Armed Bernoulli Bandits process

In mistat: Data Sets, Functions and Examples from the Book: "Modern Industrial Statistics" by Kenett, Zacks and Amberti

Bayesian One-Armed Bernoulli Bandits process

Description

Simulate the expected number of trials on arm B before switching to the known arm A, and the expected reward.

Usage

simOAB(N, p, al, k, gam, Ns)

Arguments

N	number of trials.
p	the probability of reward on arm B (unknown).
al	the known probability of reward on arm A.
k	the initial sample size on arm B.
gam	Bayesian confidence level.
Ns	number of runs in the simulation.

Value

MeanValueStoppingTime	mean value at the stopping time
StandardDeviationST	standard deviation of the value at the stopping time
MeanValueExpectedReward	mean value of the expected reward
StandardDeviationST	standard deviation of the expected reward

Author(s)

Shelemyahu Zacks

See Also

dynOAB

Examples

set.seed(123)
simOAB(N = 50, p = 0.6, al = 0.5, k = 10, gam = 0.95, Ns = 1000)

mistat documentation built on March 7, 2023, 6:43 p.m.