1 #ifndef STAN_MATH_REV_SCAL_FUN_LOG_FALLING_FACTORIAL_HPP 2 #define STAN_MATH_REV_SCAL_FUN_LOG_FALLING_FACTORIAL_HPP 22 avi_->
adj_ = std::numeric_limits<double>::quiet_NaN();
23 bvi_->
adj_ = std::numeric_limits<double>::quiet_NaN();
39 avi_->
adj_ = std::numeric_limits<double>::quiet_NaN();
52 bvi_->
adj_ = std::numeric_limits<double>::quiet_NaN();
fvar< T > log_falling_factorial(const fvar< T > &x, const fvar< T > &n)
The variable implementation base class.
bool is_any_nan(const T &x)
Returns true if the input is NaN and false otherwise.
Independent (input) and dependent (output) variables for gradients.
const double val_
The value of this variable.
void chain()
Apply the chain rule to this variable based on the variables on which it depends. ...
log_falling_factorial_dv_vari(double a, vari *bvi)
void chain()
Apply the chain rule to this variable based on the variables on which it depends. ...
vari * vi_
Pointer to the implementation of this variable.
log_falling_factorial_vd_vari(vari *avi, double b)
log_falling_factorial_vv_vari(vari *avi, vari *bvi)
double adj_
The adjoint of this variable, which is the partial derivative of this variable with respect to the ro...
fvar< T > digamma(const fvar< T > &x)
Return the derivative of the log gamma function at the specified argument.
void chain()
Apply the chain rule to this variable based on the variables on which it depends. ...