# best_binomial_bandit {bandit}

### Description

Compute the Bayesian probabilities for each arm being the best binomial bandit.

### Usage

best_binomial_bandit(x, n, alpha=1, beta=1)

### Arguments

- x
- as in prop.test, a vector of the number of successes
- n
- as in prop.test, a vector of the number of trials
- alpha
- shape parameter alpha for the prior beta distribution.
- beta
- shape parameter beta for the prior beta distribution.

### Values

a vector of probabilities for each arm being the best binomial bandit; this can be used for future randomized allocation

### References

Steven L. Scott, A modern Bayesian look at the multi-armed bandit, Appl. Stochastic Models Bus. Ind. 2010; 26:639-658. (http://www.economics.uci.edu/~ivan/asmb.874.pdf)

### See Also

### Examples

x=c(10,20,30,50) n=c(100,102,120,130) arm_probabilities = best_binomial_bandit(x,n) print(arm_probabilities) paste("The best arm is likely ", which.max(arm_probabilities), ", with ", round(100*max(arm_probabilities), 2), " percent probability of being the best.", sep="") best_binomial_bandit(c(2,20),c(100,1000)) best_binomial_bandit(c(2,20),c(100,1000), alpha = 2, beta = 5) #quick look at the various shapes of the beta distribution as we change the shape params: AlphaBeta = cbind(alpha=c(0.5,5,1,2,2),beta=c(0.5,1,3,2,5)) M = nrow(AlphaBeta) y= matrix(0,100,ncol=M) x = seq(0,1,length=100) for (i in 1:M) y[,i] = dbeta(x,AlphaBeta[i,1],AlphaBeta[i,2]) matplot(x,y,type="l", ylim = c(0,3.5), lty=1, lwd=2) param_strings = paste("a=", AlphaBeta[,"alpha"], ", b=", AlphaBeta[,"beta"], sep="") legend("top", legend = param_strings, col=1:M, lty=1)

Documentation reproduced from package bandit, version 0.5.0. License: GPL-3