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reverse mode automatic differentiation
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math
fwd
scal
fun
log_falling_factorial.hpp
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#ifndef STAN_MATH_FWD_SCAL_FUN_LOG_FALLING_FACTORIAL_HPP
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#define STAN_MATH_FWD_SCAL_FUN_LOG_FALLING_FACTORIAL_HPP
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#include <
stan/math/fwd/core.hpp
>
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#include <
stan/math/prim/scal/fun/log_falling_factorial.hpp
>
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#include <boost/math/special_functions/digamma.hpp>
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namespace
stan
{
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namespace
math {
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template
<
typename
T>
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inline
fvar<T>
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log_falling_factorial
(
const
fvar<T>
& x,
const
fvar<T>
& n) {
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using
boost::math::digamma
;
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return
fvar<T>
(
log_falling_factorial
(x.
val_
, n.
val_
),
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(
digamma
(x.
val_
+ 1)
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-
digamma
(x.
val_
- n.
val_
+ 1)) * x.
d_
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+
digamma
(x.
val_
- n.
val_
+ 1) * n.
d_
);
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}
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template
<
typename
T>
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inline
fvar<T>
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log_falling_factorial
(
const
double
x,
const
fvar<T>
& n) {
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using
boost::math::digamma
;
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return
fvar<T>
(
log_falling_factorial
(x, n.
val_
),
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digamma
(x - n.
val_
+ 1) * n.
d_
);
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}
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template
<
typename
T>
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inline
fvar<T>
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log_falling_factorial
(
const
fvar<T>
& x,
const
double
n) {
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using
boost::math::digamma
;
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return
fvar<T>
(
log_falling_factorial
(x.
val_
, n),
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(
digamma
(x.
val_
+ 1)
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-
digamma
(x.
val_
- n + 1)) * x.
d_
);
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}
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}
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}
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#endif
core.hpp
stan::math::log_falling_factorial
fvar< T > log_falling_factorial(const fvar< T > &x, const fvar< T > &n)
Definition:
log_falling_factorial.hpp:14
stan::math::fvar::d_
T d_
Definition:
fvar.hpp:14
stan
Definition:
log_sum_exp.hpp:8
stan::math::fvar::val_
T val_
Definition:
fvar.hpp:13
log_falling_factorial.hpp
stan::math::fvar
Definition:
fvar.hpp:12
stan::math::digamma
fvar< T > digamma(const fvar< T > &x)
Definition:
digamma.hpp:15
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