vg
tools for working with variation graphs
Classes | Namespaces | Typedefs | Functions
distributions.hpp File Reference
#include <map>
#include <cmath>
#include "utility.hpp"

Classes

class  vg::uniform_real_distribution< T >
 
class  vg::normal_distribution< T >
 
class  vg::WideningPRNG< PRNG, OutType >
 
class  vg::uniform_int_distribution< T >
 
class  vg::discrete_distribution< T >
 We provide a partial discrete_distribution implementation that is just the parts we need. More...
 

Namespaces

 vg
 

Typedefs

using vg::real_t = long double
 

Functions

real_t vg::gamma_ln (real_t x)
 
real_t vg::factorial_ln (int n)
 
real_t vg::pow_ln (real_t m, int n)
 
real_t vg::choose_ln (int n, int k)
 
real_t vg::multinomial_choose_ln (int n, vector< int > k)
 
real_t vg::poisson_prob_ln (int observed, real_t expected)
 
template<typename ProbIn >
real_t vg::multinomial_sampling_prob_ln (const vector< ProbIn > &probs, const vector< int > &obs)
 
template<typename ProbIn >
real_t vg::binomial_cmf_ln (ProbIn success_logprob, size_t trials, size_t successes)
 
template<typename ProbIn >
real_t vg::geometric_sampling_prob_ln (ProbIn success_logprob, size_t trials)
 
template<typename Iter >
bool vg::advance_split (Iter start, Iter end)
 
template<typename ProbIn >
real_t vg::multinomial_censored_sampling_prob_ln (const vector< ProbIn > &probs, const unordered_map< vector< bool >, int > &obs)
 
real_t vg::ewens_af_prob_ln (const vector< int > &a, real_t theta)