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I am using a simple implementation of Bayes theorem to find the discrete probability distribution of proportion of wins.

naiveBayes <- function(theta,prior,win=T)
    {
      ifelse(win==T,likelihood <- theta,likelihood <- 1-theta)
      constant <- sum(prior * likelihood)
      posterior <- (prior * likelihood) / constant
      posterior
    }

A win being True or FALSE gives a posterior which I will use a prior for the second computation and so on.

Question: How to give more weight to recent event? Say, the new event is a loss (win=F), I would like it to have higher influence than what the bayes theorem provides.

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