Public
Edited
Jun 3, 2023
min_entropy_posterior_alpha = snake_bite_deaths + 1
min_entropy_posterior_beta = sample_size - snake_bite_deaths + 1
subjective_posterior_alpha = prior_alpha + snake_bite_deaths
subjective_posterior_beta = prior_beta + sample_size - snake_bite_deaths
{
}
mean_estimate = portion_snake_bites
var_estimate = portion_snake_bites * (1 - portion_snake_bites)/ sample_size
confidence_interval = [mean_estimate - 1.96 * Math.sqrt(var_estimate), mean_estimate + 1.96 * Math.sqrt(var_estimate)]
significance = 0.05
power = 0.8
jstat = require('jstat')
beta_binomial_pdf = (x, n, alpha, beta) => Math.exp(jstat.factorialln(n) - jstat.factorialln(x) - jstat.factorialln(n - x) + jstat.betaln(x + alpha,n - x + beta) - jstat.betaln(alpha, beta))
lnBetaFunc = (a, b) => {
// Log Beta Function
// ln(Beta(x,y))
let foo = 0.0;
for (let i=0; i<a-2; i++) {
foo += Math.log(a-1-i);
}
for (let i=0; i<b-2; i++) {
foo += Math.log(b-1-i);
}
for (let i=0; i<a+b-2; i++) {
foo -= Math.log(a+b-1-i);
}
return foo
}
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