Stan Math Library  2.20.0
reverse mode automatic differentiation
falling_factorial.hpp
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1 #ifndef STAN_MATH_REV_SCAL_FUN_FALLING_FACTORIAL_HPP
2 #define STAN_MATH_REV_SCAL_FUN_FALLING_FACTORIAL_HPP
3 
4 #include <stan/math/rev/meta.hpp>
5 #include <stan/math/rev/core.hpp>
8 
9 namespace stan {
10 namespace math {
11 
12 namespace internal {
13 
15  public:
17  : op_vd_vari(falling_factorial(avi->val_, b), avi, b) {}
18  void chain() {
19  avi_->adj_ += adj_ * val_
20  * (digamma(avi_->val_ + 1) - digamma(avi_->val_ - bd_ + 1));
21  }
22 };
23 } // namespace internal
24 
25 inline var falling_factorial(const var& a, int b) {
27 }
28 
29 } // namespace math
30 } // namespace stan
31 #endif
void chain()
Apply the chain rule to this variable based on the variables on which it depends. ...
The variable implementation base class.
Definition: vari.hpp:30
Independent (input) and dependent (output) variables for gradients.
Definition: var.hpp:33
friend class var
Definition: vari.hpp:32
const double val_
The value of this variable.
Definition: vari.hpp:38
fvar< T > falling_factorial(const fvar< T > &x, int n)
Return autodiff variable with the gradient and result of the falling factorial function applied to th...
vari * vi_
Pointer to the implementation of this variable.
Definition: var.hpp:45
double adj_
The adjoint of this variable, which is the partial derivative of this variable with respect to the ro...
Definition: vari.hpp:44
fvar< T > digamma(const fvar< T > &x)
Return the derivative of the log gamma function at the specified argument.
Definition: digamma.hpp:23

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