20.8: Appendix-

Statistical Thinking for the 21st Century · LibreTexts

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    20.8.1 Rejection sampling

    We will generate samples from our posterior distribution using a simple algorithm known as rejection sampling. The idea is that we choose a random value of x (in this case 

    # Compute credible intervals for example

nsamples <- 100000

# create random uniform variates for x and y
x <- runif(nsamples)
y <- runif(nsamples)

# create f(x)
fx <- dbinom(x = nResponders, size = 100, prob = x)

# accept samples where y < f(x)
accept <- which(y < fx)
accepted_samples <- x[accept]

credible_interval <- quantile(x = accepted_samples, 
                              probs = c(0.025, 0.975))
kable(credible_interval)
    x
    2.5% 0.54
    98% 0.73
    Rejection sampling example.The black line shows the density of all possible values of p(respond); the blue lines show the 2.5th and 97.5th percentiles of the distribution, which represent the 95 percent credible interval for the estimate of p(respond).
    Figure 20.6: Rejection sampling example.The black line shows the density of all possible values of p(respond); the blue lines show the 2.5th and 97.5th percentiles of the distribution, which represent the 95 percent credible interval for the estimate of p(respond).

    This page titled 20.8: Appendix- was last modified on Mon, 11 Jan 2021 05:14:09 GMT and is shared under a CC BY-NC 4.0 license and was authored, remixed, and/or curated by Russell A. Poldrack via source content that was edited to the style and standards of the LibreTexts platform.