1 #ifndef STAN_MATH_REV_SCAL_FUN_LOG_FALLING_FACTORIAL_HPP 2 #define STAN_MATH_REV_SCAL_FUN_LOG_FALLING_FACTORIAL_HPP 15 class log_falling_factorial_vv_vari :
public op_vv_vari {
17 log_falling_factorial_vv_vari(vari* avi, vari* bvi) :
24 avi_->adj_ = std::numeric_limits<double>::quiet_NaN();
25 bvi_->adj_ = std::numeric_limits<double>::quiet_NaN();
29 -
digamma(avi_->val_ - bvi_->val_ + 1));
31 *
digamma(avi_->val_ - bvi_->val_ + 1);
36 class log_falling_factorial_vd_vari :
public op_vd_vari {
38 log_falling_factorial_vd_vari(vari* avi,
double b) :
44 avi_->adj_ = std::numeric_limits<double>::quiet_NaN();
48 -
digamma(avi_->val_ - bd_ + 1));
52 class log_falling_factorial_dv_vari :
public op_dv_vari {
54 log_falling_factorial_dv_vari(
double a, vari* bvi) :
60 bvi_->adj_ = std::numeric_limits<double>::quiet_NaN();
63 *
digamma(ad_ - bvi_->val_ + 1);
70 return var(
new log_falling_factorial_vd_vari(a.
vi_, b));
75 return var(
new log_falling_factorial_vv_vari(a.
vi_, b.
vi_));
80 return var(
new log_falling_factorial_dv_vari(a, b.
vi_));
fvar< T > log_falling_factorial(const fvar< T > &x, const fvar< T > &n)
Independent (input) and dependent (output) variables for gradients.
vari * vi_
Pointer to the implementation of this variable.
int is_nan(const fvar< T > &x)
Returns 1 if the input's value is NaN and 0 otherwise.
fvar< T > digamma(const fvar< T > &x)
Return the derivative of the log gamma function at the specified argument.