1 #ifndef STAN_MATH_PRIM_SCAL_PROB_STUDENT_T_LOG_HPP
2 #define STAN_MATH_PRIM_SCAL_PROB_STUDENT_T_LOG_HPP
22 #include <boost/random/student_t_distribution.hpp>
23 #include <boost/random/variate_generator.hpp>
55 template <
bool propto,
typename T_y,
typename T_dof,
56 typename T_loc,
typename T_scale>
57 typename return_type<T_y, T_dof, T_loc, T_scale>::type
59 const T_scale& sigma) {
60 static const char*
function(
"stan::math::student_t_log");
77 T_partials_return logp(0.0);
86 "Degrees of freedom parameter", nu,
87 "Location parameter", mu,
88 "Scale parameter", sigma);
98 size_t N =
max_size(y, nu, mu, sigma);
108 T_partials_return, T_dof> half_nu(
length(nu));
109 for (
size_t i = 0; i <
length(nu); i++)
111 half_nu[i] = 0.5 *
value_of(nu_vec[i]);
114 T_partials_return, T_dof> lgamma_half_nu(
length(nu));
116 T_partials_return, T_dof>
117 lgamma_half_nu_plus_half(
length(nu));
119 for (
size_t i = 0; i <
length(nu); i++) {
120 lgamma_half_nu[i] =
lgamma(half_nu[i]);
121 lgamma_half_nu_plus_half[i] =
lgamma(half_nu[i] + 0.5);
126 T_partials_return, T_dof> digamma_half_nu(
length(nu));
128 T_partials_return, T_dof>
129 digamma_half_nu_plus_half(
length(nu));
131 for (
size_t i = 0; i <
length(nu); i++) {
132 digamma_half_nu[i] =
digamma(half_nu[i]);
133 digamma_half_nu_plus_half[i] =
digamma(half_nu[i] + 0.5);
138 T_partials_return, T_dof> log_nu(
length(nu));
139 for (
size_t i = 0; i <
length(nu); i++)
144 T_partials_return, T_scale> log_sigma(
length(sigma));
145 for (
size_t i = 0; i <
length(sigma); i++)
150 T_partials_return, T_y, T_dof, T_loc, T_scale>
151 square_y_minus_mu_over_sigma__over_nu(N);
154 T_partials_return, T_y, T_dof, T_loc, T_scale>
157 for (
size_t i = 0; i < N; i++)
159 const T_partials_return y_dbl =
value_of(y_vec[i]);
160 const T_partials_return mu_dbl =
value_of(mu_vec[i]);
161 const T_partials_return sigma_dbl =
value_of(sigma_vec[i]);
162 const T_partials_return nu_dbl =
value_of(nu_vec[i]);
163 square_y_minus_mu_over_sigma__over_nu[i]
164 =
square((y_dbl - mu_dbl) / sigma_dbl) / nu_dbl;
165 log1p_exp[i] =
log1p(square_y_minus_mu_over_sigma__over_nu[i]);
169 operands_and_partials(y, nu, mu, sigma);
170 for (
size_t n = 0; n < N; n++) {
171 const T_partials_return y_dbl =
value_of(y_vec[n]);
172 const T_partials_return mu_dbl =
value_of(mu_vec[n]);
173 const T_partials_return sigma_dbl =
value_of(sigma_vec[n]);
174 const T_partials_return nu_dbl =
value_of(nu_vec[n]);
178 logp += lgamma_half_nu_plus_half[n] - lgamma_half_nu[n]
181 logp -= log_sigma[n];
183 logp -= (half_nu[n] + 0.5)
187 operands_and_partials.
d_x1[n]
189 * 1.0 / (1.0 + square_y_minus_mu_over_sigma__over_nu[n])
190 * (2.0 * (y_dbl - mu_dbl) /
square(sigma_dbl) / nu_dbl);
193 const T_partials_return inv_nu = 1.0 / nu_dbl;
194 operands_and_partials.
d_x2[n]
195 += 0.5*digamma_half_nu_plus_half[n] - 0.5*digamma_half_nu[n]
199 * (1.0/(1.0 + square_y_minus_mu_over_sigma__over_nu[n])
200 * square_y_minus_mu_over_sigma__over_nu[n] * inv_nu);
203 operands_and_partials.
d_x3[n]
204 -= (half_nu[n] + 0.5)
205 / (1.0 + square_y_minus_mu_over_sigma__over_nu[n])
206 * (2.0 * (mu_dbl - y_dbl) / (sigma_dbl*sigma_dbl*nu_dbl));
209 const T_partials_return inv_sigma = 1.0 / sigma_dbl;
210 operands_and_partials.
d_x4[n]
212 + (nu_dbl + 1.0) / (1.0 + square_y_minus_mu_over_sigma__over_nu[n])
213 * (square_y_minus_mu_over_sigma__over_nu[n] * inv_sigma);
216 return operands_and_partials.
value(logp);
219 template <
typename T_y,
typename T_dof,
typename T_loc,
typename T_scale>
223 const T_scale& sigma) {
224 return student_t_log<false>(y, nu, mu, sigma);
VectorView< T_return_type, false, true > d_x2
fvar< T > lgamma(const fvar< T > &x)
bool check_not_nan(const char *function, const char *name, const T_y &y)
Return true if y is not NaN.
T value_of(const fvar< T > &v)
Return the value of the specified variable.
fvar< T > log(const fvar< T > &x)
T_return_type value(double value)
Returns a T_return_type with the value specified with the partial derivatves.
const double NEG_LOG_SQRT_PI
size_t length(const std::vector< T > &x)
Template metaprogram to calculate whether a summand needs to be included in a proportional (log) prob...
boost::math::tools::promote_args< typename scalar_type< T1 >::type, typename scalar_type< T2 >::type, typename scalar_type< T3 >::type, typename scalar_type< T4 >::type, typename scalar_type< T5 >::type, typename scalar_type< T6 >::type >::type type
fvar< T > square(const fvar< T > &x)
Metaprogram to determine if a type has a base scalar type that can be assigned to type double...
This class builds partial derivatives with respect to a set of operands.
VectorView< T_return_type, false, true > d_x3
size_t max_size(const T1 &x1, const T2 &x2)
bool check_finite(const char *function, const char *name, const T_y &y)
Return true if y is finite.
VectorBuilder allocates type T1 values to be used as intermediate values.
fvar< T > log1p_exp(const fvar< T > &x)
bool check_consistent_sizes(const char *function, const char *name1, const T1 &x1, const char *name2, const T2 &x2)
Return true if the dimension of x1 is consistent with x2.
fvar< T > log1p(const fvar< T > &x)
return_type< T_y, T_dof, T_loc, T_scale >::type student_t_log(const T_y &y, const T_dof &nu, const T_loc &mu, const T_scale &sigma)
The log of the Student-t density for the given y, nu, mean, and scale parameter.
VectorView is a template expression that is constructed with a container or scalar, which it then allows to be used as an array using operator[].
bool check_positive_finite(const char *function, const char *name, const T_y &y)
Return true if y is positive and finite.
VectorView< T_return_type, false, true > d_x1
fvar< T > digamma(const fvar< T > &x)
VectorView< T_return_type, false, true > d_x4