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Merge pull request #3108 from lingium/feature/issue-3107-beta-neg-binomial-lpmf
Feature/issue-3107 add beta negative binomial lpmf
2 parents 67d3c88 + 25650dc commit 9c7c3ff

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‎stan/math/prim/prob.hpp‎

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#include <stan/math/prim/prob/beta_lccdf.hpp>
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#include <stan/math/prim/prob/beta_lcdf.hpp>
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#include <stan/math/prim/prob/beta_lpdf.hpp>
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#include <stan/math/prim/prob/beta_neg_binomial_lpmf.hpp>
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#include <stan/math/prim/prob/beta_proportion_ccdf_log.hpp>
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#include <stan/math/prim/prob/beta_proportion_cdf_log.hpp>
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#include <stan/math/prim/prob/beta_proportion_lccdf.hpp>

‎stan/math/prim/prob/beta_neg_binomial_lpmf.hpp‎

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#ifndef STAN_MATH_PRIM_PROB_BETA_NEG_BINOMIAL_LPMF_HPP
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#define STAN_MATH_PRIM_PROB_BETA_NEG_BINOMIAL_LPMF_HPP
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#include <stan/math/prim/meta.hpp>
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#include <stan/math/prim/err.hpp>
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#include <stan/math/prim/fun/constants.hpp>
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#include <stan/math/prim/fun/digamma.hpp>
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#include <stan/math/prim/fun/lbeta.hpp>
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#include <stan/math/prim/fun/lgamma.hpp>
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#include <stan/math/prim/fun/max_size.hpp>
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#include <stan/math/prim/fun/scalar_seq_view.hpp>
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#include <stan/math/prim/fun/size.hpp>
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#include <stan/math/prim/fun/size_zero.hpp>
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#include <stan/math/prim/fun/value_of.hpp>
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#include <stan/math/prim/functor/partials_propagator.hpp>
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namespace stan {
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namespace math {
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/** \ingroup prob_dists
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* Returns the log PMF of the Beta Negative Binomial distribution with given
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* number of successes, prior success, and prior failure parameters.
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* Given containers of matching sizes, returns the log sum of probabilities.
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*
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* @tparam T_n type of failure parameter
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* @tparam T_r type of number of successes parameter
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* @tparam T_alpha type of prior success parameter
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* @tparam T_beta type of prior failure parameter
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*
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* @param n failure parameter
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* @param r Number of successes parameter
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* @param alpha prior success parameter
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* @param beta prior failure parameter
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* @return log probability or log sum of probabilities
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* @throw std::domain_error if r, alpha, or beta fails to be positive
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* @throw std::invalid_argument if container sizes mismatch
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*/
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template <bool propto, typename T_n, typename T_r, typename T_alpha,
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typename T_beta,
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require_all_not_nonscalar_prim_or_rev_kernel_expression_t<
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T_n, T_r, T_alpha, T_beta>* = nullptr>
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inline return_type_t<T_r, T_alpha, T_beta> beta_neg_binomial_lpmf(
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const T_n& n, const T_r& r, const T_alpha& alpha, const T_beta& beta) {
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using T_partials_return = partials_return_t<T_n, T_r, T_alpha, T_beta>;
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using T_n_ref = ref_type_t<T_n>;
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using T_r_ref = ref_type_t<T_r>;
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using T_alpha_ref = ref_type_t<T_alpha>;
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using T_beta_ref = ref_type_t<T_beta>;
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static constexpr const char* function = "beta_neg_binomial_lpmf";
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check_consistent_sizes(
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function, "Failures variable", n, "Number of successes parameter", r,
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"Prior success parameter", alpha, "Prior failure parameter", beta);
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if (size_zero(n, r, alpha, beta)) {
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return 0.0;
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}
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T_n_ref n_ref = n;
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T_r_ref r_ref = r;
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T_alpha_ref alpha_ref = alpha;
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T_beta_ref beta_ref = beta;
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check_nonnegative(function, "Failures variable", n_ref);
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check_positive_finite(function, "Number of successes parameter", r_ref);
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check_positive_finite(function, "Prior success parameter", alpha_ref);
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check_positive_finite(function, "Prior failure parameter", beta_ref);
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if constexpr (!include_summand<propto, T_r, T_alpha, T_beta>::value) {
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return 0.0;
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}
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auto ops_partials = make_partials_propagator(r_ref, alpha_ref, beta_ref);
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scalar_seq_view<T_n> n_vec(n);
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scalar_seq_view<T_r_ref> r_vec(r_ref);
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scalar_seq_view<T_alpha_ref> alpha_vec(alpha_ref);
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scalar_seq_view<T_beta_ref> beta_vec(beta_ref);
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const size_t max_size_seq_view = max_size(n, r, alpha, beta);
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T_partials_return logp(0.0);
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for (size_t i = 0; i < max_size_seq_view; i++) {
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if constexpr (include_summand<propto>::value) {
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logp -= lgamma(n_vec[i] + 1);
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}
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T_partials_return lbeta_denominator = lbeta(r_vec.val(i), alpha_vec.val(i));
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T_partials_return lgamma_numerator = lgamma(n_vec[i] + beta_vec.val(i));
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T_partials_return lgamma_denominator = lgamma(beta_vec.val(i));
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T_partials_return lbeta_numerator
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= lbeta(n_vec[i] + r_vec.val(i), alpha_vec.val(i) + beta_vec.val(i));
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logp += lbeta_numerator + lgamma_numerator - lbeta_denominator
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- lgamma_denominator;
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if (!is_constant_all<T_r, T_alpha, T_beta>::value) {
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T_partials_return digamma_n_r_alpha_beta = digamma(
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n_vec[i] + r_vec.val(i) + alpha_vec.val(i) + beta_vec.val(i));
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if constexpr (!is_constant<T_r>::value || !is_constant<T_alpha>::value) {
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T_partials_return digamma_r_alpha
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= digamma(r_vec.val(i) + alpha_vec.val(i));
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if constexpr (!is_constant_all<T_r>::value) {
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partials<0>(ops_partials)[i]
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+= digamma(n_vec[i] + r_vec.val(i)) - digamma_n_r_alpha_beta
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- (digamma(r_vec.val(i)) - digamma_r_alpha);
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}
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if constexpr (!is_constant_all<T_alpha>::value) {
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partials<1>(ops_partials)[i]
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+= -digamma_n_r_alpha_beta
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- (digamma(alpha_vec.val(i)) - digamma_r_alpha);
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}
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}
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if constexpr (!is_constant<T_beta>::value
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|| !is_constant<T_alpha>::value) {
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T_partials_return digamma_alpha_beta
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= digamma(alpha_vec.val(i) + beta_vec.val(i));
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if constexpr (!is_constant_all<T_beta>::value) {
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partials<2>(ops_partials)[i] += digamma_alpha_beta
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- digamma_n_r_alpha_beta
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+ digamma(n_vec[i] + beta_vec.val(i))
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- digamma(beta_vec.val(i));
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}
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if constexpr (!is_constant_all<T_alpha>::value) {
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partials<1>(ops_partials)[i] += digamma_alpha_beta;
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}
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}
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}
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}
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return ops_partials.build(logp);
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}
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template <typename T_n, typename T_r, typename T_alpha, typename T_beta>
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inline return_type_t<T_r, T_alpha, T_beta> beta_neg_binomial_lpmf(
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const T_n& n, const T_r& r, const T_alpha& alpha, const T_beta& beta) {
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return beta_neg_binomial_lpmf<false>(n, r, alpha, beta);
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}
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} // namespace math
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} // namespace stan
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#endif

‎test/prob/beta_neg_binomial/beta_neg_binomial_test.hpp‎

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// Arguments: Ints, Doubles, Doubles, Doubles
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#include <stan/math/prim/prob/beta_neg_binomial_lpmf.hpp>
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#include <stan/math/prim/fun/lbeta.hpp>
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#include <stan/math/prim/fun/lgamma.hpp>
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using stan::math::var;
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using std::numeric_limits;
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using std::vector;
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class AgradDistributionsBetaNegBinomial : public AgradDistributionTest {
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public:
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void valid_values(vector<vector<double> >& parameters,
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vector<double>& log_prob) {
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vector<double> param(4);
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param[0] = 5; // n
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param[1] = 20.0; // r
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param[2] = 10.0; // alpha
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param[3] = 25.0; // beta
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parameters.push_back(param);
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log_prob.push_back(-10.3681267949788); // expected log_prob
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param[0] = 10; // n
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param[1] = 5.5; // r
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param[2] = 2.5; // alpha
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param[3] = 0.5; // beta
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parameters.push_back(param);
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log_prob.push_back(-5.166741878823932); // expected log_prob
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}
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void invalid_values(vector<size_t>& index, vector<double>& value) {
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// n
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index.push_back(0U);
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value.push_back(-1);
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// r
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index.push_back(1U);
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value.push_back(0.0);
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index.push_back(1U);
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value.push_back(-1.0);
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index.push_back(1U);
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value.push_back(std::numeric_limits<double>::infinity());
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// alpha
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index.push_back(2U);
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value.push_back(0.0);
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index.push_back(2U);
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value.push_back(-1.0);
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index.push_back(2U);
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value.push_back(std::numeric_limits<double>::infinity());
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// beta
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index.push_back(3U);
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value.push_back(0.0);
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index.push_back(3U);
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value.push_back(-1.0);
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index.push_back(3U);
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value.push_back(std::numeric_limits<double>::infinity());
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}
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template <class T_n, class T_r, class T_size1, class T_size2, typename T4,
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typename T5>
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stan::return_type_t<T_r, T_size1, T_size2> log_prob(const T_n& n,
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const T_r& r,
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const T_size1& alpha,
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const T_size2& beta,
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const T4&, const T5&) {
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return stan::math::beta_neg_binomial_lpmf(n, r, alpha, beta);
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}
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template <bool propto, class T_n, class T_r, class T_size1, class T_size2,
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typename T4, typename T5>
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stan::return_type_t<T_r, T_size1, T_size2> log_prob(const T_n& n,
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const T_r& r,
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const T_size1& alpha,
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const T_size2& beta,
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const T4&, const T5&) {
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return stan::math::beta_neg_binomial_lpmf<propto>(n, r, alpha, beta);
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}
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template <class T_n, class T_r, class T_size1, class T_size2, typename T4,
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typename T5>
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stan::return_type_t<T_r, T_size1, T_size2> log_prob_function(
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const T_n& n, const T_r& r, const T_size1& alpha, const T_size2& beta,
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const T4&, const T5&) {
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using stan::math::lbeta;
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using stan::math::lgamma;
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return lbeta(n + r, alpha + beta) - lbeta(r, alpha) + lgamma(n + beta)
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- lgamma(n + 1) - lgamma(beta);
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}
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};

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