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Generates a plot that shows the bandit posterior values as they are sequentially updated by the provided win / loss data.
plot_bandit_posterior( data, prior = c(m1_good = 0.5, m2_good = 0.5), win_probs = c(good = 1/2, bad = 1/3) )
data frame containing win loss data
prior vector containing the probabilities of Machine 1 and Machine 2 being good, defaults to 50-50.
vector containing the probabilities of winning on the good and bad machine respectively.
# NOT RUN { data = data.frame(machine = c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L), outcome = c("W", "L", "W", "L", "L", "W", "L", "L", "L", "W")) plot_bandit_posterior(data) # }
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