diff --git a/Makefile b/Makefile index 51b9a50..21f4a96 100644 --- a/Makefile +++ b/Makefile @@ -7,7 +7,7 @@ cov: poetry run pytest --cov-report html --cov=conjugate tests && open htmlcov/index.html format: - poetry run black conjugate tests + poetry run pre-commit run --all-files html: open http://localhost:8000/ diff --git a/conjugate/models.py b/conjugate/models.py index b454632..898aa08 100644 --- a/conjugate/models.py +++ b/conjugate/models.py @@ -64,10 +64,44 @@ def binomial_beta_posterior_predictive(n: NUMERIC, beta: Beta) -> BetaBinomial: return BetaBinomial(n=n, alpha=beta.alpha, beta=beta.beta) +def bernoulli_beta(x: NUMERIC, beta_prior: Beta) -> Beta: + """Posterior distribution for a bernoulli likelihood with a beta prior. + + Args: + x: sucesses from that trials + beta_prior: Beta distribution prior + + Returns: + Beta distribution posterior + + """ + return binomial_beta(n=1, x=x, beta_prior=beta_prior) + + +def bernoulli_beta_posterior_predictive(beta: Beta) -> BetaBinomial: + """Posterior predictive distribution for a bernoulli likelihood with a beta prior. + + Args: + beta: Beta distribution + + Returns: + BetaBinomial posterior predictive distribution + + """ + return binomial_beta_posterior_predictive(n=1, beta=beta) + + def negative_binomial_beta(r, n, x, beta_prior: Beta) -> Beta: """Posterior distribution for a negative binomial likelihood with a beta prior. Args: + r: number of failures + n: number of trials + x: number of successes + beta_prior: Beta distribution prior + + Returns: + Beta distribution posterior """ alpha_post = beta_prior.alpha + (r * n) @@ -117,7 +151,16 @@ def get_categorical_dirichlet_posterior_params( def categorical_dirichlet(x: NUMERIC, dirichlet_prior: Dirichlet) -> Dirichlet: - """Posterior distribution of Categorical model with Dirichlet prior.""" + """Posterior distribution of Categorical model with Dirichlet prior. + + Args: + x: counts + dirichlet_prior: Dirichlet prior on the counts + + Returns: + Dirichlet posterior distribution + + """ alpha_post = get_dirichlet_posterior_params(dirichlet_prior.alpha, x) return Dirichlet(alpha=alpha_post) @@ -155,7 +198,17 @@ def get_poisson_gamma_posterior_params( def poisson_gamma(x_total: NUMERIC, n: NUMERIC, gamma_prior: Gamma) -> Gamma: - """Posterior distribution for a poisson likelihood with a gamma prior""" + """Posterior distribution for a poisson likelihood with a gamma prior. + + Args: + x_total: sum of all outcomes + n: total number of samples in x_total + gamma_prior: Gamma prior + + Returns: + Gamma posterior distribution + + """ alpha_post, beta_post = get_poisson_gamma_posterior_params( alpha=gamma_prior.alpha, beta=gamma_prior.beta, x_total=x_total, n=n ) @@ -187,8 +240,18 @@ def poisson_gamma_posterior_predictive( get_exponential_gamma_posterior_params = get_poisson_gamma_posterior_params -def exponetial_gamma(x_total: NUMERIC, n: NUMERIC, gamma_prior: Gamma) -> Gamma: - """Posterior distribution for an exponential likelihood with a gamma prior""" +def exponential_gamma(x_total: NUMERIC, n: NUMERIC, gamma_prior: Gamma) -> Gamma: + """Posterior distribution for an exponential likelihood with a gamma prior. + + Args: + x_total: sum of all outcomes + n: total number of samples in x_total + gamma_prior: Gamma prior + + Returns: + Gamma posterior distribution + + """ alpha_post, beta_post = get_exponential_gamma_posterior_params( 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