# How to fit a beta-binomial model in python

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Answer

To fit a beta-binomial model in Python, you can use the
This code will output the maximum likelihood estimates for the alpha and beta parameters of the beta-binomial model.

`scipy.stats.betabinom`

function. The beta-binomial distribution is a binomial distribution with a probability of success that follows a beta distribution. Here's a step-by-step guide to fitting a beta-binomial model:
- Import the necessary libraries:

python`import numpy as np from scipy.stats import betabinom`

- Define the number of trials (n), the number of successes (k), and the alpha and beta parameters (a and b).

python`n, k, a, b = 100, 60, 2.3, 0.63`

- Calculate the probability mass function (PMF) for the given data:

python`pmf = betabinom.pmf(k, n, a, b)`

- Calculate the negative log-likelihood function:

python`def bbll(parms, k, n): alpha, beta = parms ll = betabinom.logpmf(k, n, alpha, beta) return -ll.sum()`

- Use the
`minimize`

function to find the maximum likelihood estimates for the alpha and beta parameters:

python`result = minimize(bbll, [1, 1], args=(pmf, n), method='Nelder-Mead')`

- Print the results:

python`print(result.x)`

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