oneapi::mkl::rng::negative_binomial¶
Generates random numbers with negative binomial distribution.
Syntax
template<typename IntType = std::int32_t, typename Method = negative_binomial_method::by_default>
class negative_binomial {
public:
using method_type = Method;
using result_type = IntType;
negative_binomial(): negative_binomial(0.1, 0.5){}
explicit negative_binomial(double a, double p);
double a() const;
double p() const;
};
Devices supported: Host and CPU
Include Files
oneapi/mkl/rng.hpp
Description
The oneapi::mkl::rng::negative_binomial class object is used in the
oneapi::mkl::rng::generate function to provide random numbers with
negative binomial distribution and distribution parameters a
and
p
, where p
, a
∈R
; 0 < p
< 1; a
> 0.
If the first distribution parameter a
∈N
, this
distribution is the same as Pascal distribution. If a
∈N
,
the distribution can be interpreted as the expected time of a
-th
success in a sequence of Bernoulli trials, when the probability of
success is p
.
The probability distribution is given by:
The cumulative distribution function is as follows:
Intel’s compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice. Notice revision #20110804 |
This notice covers the following instruction sets: SSE2, SSE4.2, AVX2, AVX-512.
Template Parameters
|
Type of the produced values. The specific values are as follows: |
---|---|
|
Generation method. The specific values are as follows: |
Input Parameters
Name |
Type |
Description |
---|---|---|
a |
|
The first distribution parameter |
p |
|
The second distribution parameter |