[rand.dist.samp]

# 29 Numerics library [[numerics]](./#numerics)

## 29.5 Random number generation [[rand]](rand#dist.samp)

### 29.5.9 Random number distribution class templates [[rand.dist]](rand.dist#samp)

#### 29.5.9.6 Sampling distributions [rand.dist.samp]

#### [29.5.9.6.1](#discrete) Class template discrete_distribution [[rand.dist.samp.discrete]](rand.dist.samp.discrete)

[1](#discrete-1)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6273)

A discrete_distribution random number distribution
produces random integers i, 0≤i<n,
distributed according to
the discrete probability function in Formula [29.19](#eq:rand.dist.samp.discrete)[.](#discrete-1.sentence-1)

P(i|p0,…,pn−1)=pi(29.19)

[2](#discrete-2)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6282)

Unless specified otherwise,
the distribution parameters are calculated as:pk=wk/S for k=0,…,n−1,
in which the values wk,
commonly known as the [*weights*](#def:weights), shall be non-negative, non-NaN, and non-infinity[.](#discrete-2.sentence-1)

Moreover, the following relation shall hold:0<S=w0+⋯+wn−1[.](#discrete-2.sentence-2)

[🔗](#lib:discrete_distribution_)

namespace std {template<class IntType = int>class discrete_distribution {public:// typesusing result_type = IntType; using param_type = *unspecified*; // constructor and reset functions discrete_distribution(); template<class InputIterator> discrete_distribution(InputIterator firstW, InputIterator lastW);
 discrete_distribution(initializer_list<double> wl); template<class UnaryOperation> discrete_distribution(size_t nw, double xmin, double xmax, UnaryOperation fw); explicit discrete_distribution(const param_type& parm); void reset(); // equality operatorsfriend bool operator==(const discrete_distribution& x, const discrete_distribution& y); // generating functionstemplate<class URBG> result_type operator()(URBG& g); template<class URBG> result_type operator()(URBG& g, const param_type& parm); // property functions vector<double> probabilities() const;
 param_type param() const; void param(const param_type& parm);
 result_type min() const;
 result_type max() const; // inserters and extractorstemplate<class charT, class traits>friend basic_ostream<charT, traits>&operator<<(basic_ostream<charT, traits>& os, const discrete_distribution& x); template<class charT, class traits>friend basic_istream<charT, traits>&operator>>(basic_istream<charT, traits>& is, discrete_distribution& x); };}

[🔗](#lib:discrete_distribution,constructor)

`discrete_distribution();
`

[3](#discrete-3)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6346)

*Effects*: Constructs a discrete_distribution object
with n=1 and p0=1[.](#discrete-3.sentence-1)

[*Note [1](#discrete-note-1)*:

Such an object will always deliver the value 0[.](#discrete-3.sentence-2)

— *end note*]

[🔗](#lib:discrete_distribution,constructor_)

`template<class InputIterator>
  discrete_distribution(InputIterator firstW, InputIterator lastW);
`

[4](#discrete-4)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6363)

*Mandates*: is_convertible_v<iterator_traits<InputIterator>​::​value_type,double> is true[.](#discrete-4.sentence-1)

[5](#discrete-5)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6368)

*Preconditions*: InputIterator meets the [*Cpp17InputIterator*](input.iterators#:Cpp17InputIterator "24.3.5.3 Input iterators [input.iterators]") requirements ([[input.iterators]](input.iterators "24.3.5.3 Input iterators"))[.](#discrete-5.sentence-1)

If firstW == lastW,
 let n=1 and w0=1[.](#discrete-5.sentence-2)

Otherwise, [firstW, lastW) forms a sequence w of length n>0[.](#discrete-5.sentence-3)

[6](#discrete-6)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6378)

*Effects*: Constructs a discrete_distribution object
with probabilities given by the Formula [29.19](#eq:rand.dist.samp.discrete)[.](#discrete-6.sentence-1)

[🔗](#lib:discrete_distribution,constructor__)

`discrete_distribution(initializer_list<double> wl);
`

[7](#discrete-7)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6391)

*Effects*: Same as discrete_distribution(wl.begin(), wl.end())[.](#discrete-7.sentence-1)

[🔗](#lib:discrete_distribution,constructor___)

`template<class UnaryOperation>
  discrete_distribution(size_t nw, double xmin, double xmax, UnaryOperation fw);
`

[8](#discrete-8)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6403)

*Mandates*: is_invocable_r_v<double, UnaryOperation&, double> is true[.](#discrete-8.sentence-1)

[9](#discrete-9)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6407)

*Preconditions*: If nw=0, let n=1, otherwise let n=nw[.](#discrete-9.sentence-1)

The relation 0<δ=(xmax−xmin)/n holds[.](#discrete-9.sentence-2)

[10](#discrete-10)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6414)

*Effects*: Constructs a discrete_distribution object
 with probabilities given by the formula above,
 using the following values:
 If nw=0,
 let w0=1[.](#discrete-10.sentence-1)

Otherwise,
 let wk=fw(xmin+kâ‹Î´+δ/2) for k=0,…,n−1[.](#discrete-10.sentence-2)

[11](#discrete-11)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6425)

*Complexity*: The number of invocations of fw does not exceed n[.](#discrete-11.sentence-1)

[🔗](#lib:probabilities,discrete_distribution)

`vector<double> probabilities() const;
`

[12](#discrete-12)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6436)

*Returns*: A vector<double> whose size member returns n and whose operator[] member returns pk when invoked with argument k for k=0,…,n−1[.](#discrete-12.sentence-1)

#### [29.5.9.6.2](#pconst) Class template piecewise_constant_distribution [[rand.dist.samp.pconst]](rand.dist.samp.pconst)

[1](#pconst-1)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6452)

A piecewise_constant_distribution random number distribution
produces random numbers x,b0≤x<bn,
uniformly distributed over each subinterval[bi,bi+1) according to the probability density function in Formula [29.20](#eq:rand.dist.samp.pconst)[.](#pconst-1.sentence-1)

p(x|b0,…,bn,ρ0,…,ρn−1)=ρi , for bi≤x<bi+1(29.20)

[2](#pconst-2)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6464)

The n+1 distribution parameters bi,
also known as this distribution's [*interval boundaries*](#def:interval_boundaries), shall satisfy the relationbi<bi+1 for i=0,…,n−1[.](#pconst-2.sentence-1)

Unless specified otherwise,
the remaining n distribution parameters are calculated as:  
ρk=wkSâ‹(bk+1−bk) for k=0,…,n−1 , in which the values wk,
commonly known as the [*weights*](#def:weights), shall be non-negative, non-NaN, and non-infinity[.](#pconst-2.sentence-2)

Moreover, the following relation shall hold: 0<S=w0+⋯+wn−1[.](#pconst-2.sentence-3)

[🔗](#lib:piecewise_constant_distribution_)

namespace std {template<class RealType = double>class piecewise_constant_distribution {public:// typesusing result_type = RealType; using param_type = *unspecified*; // constructor and reset functions piecewise_constant_distribution(); template<class InputIteratorB, class InputIteratorW> piecewise_constant_distribution(InputIteratorB firstB, InputIteratorB lastB,
 InputIteratorW firstW); template<class UnaryOperation> piecewise_constant_distribution(initializer_list<RealType> bl, UnaryOperation fw); template<class UnaryOperation> piecewise_constant_distribution(size_t nw, RealType xmin, RealType xmax,
 UnaryOperation fw); explicit piecewise_constant_distribution(const param_type& parm); void reset(); // equality operatorsfriend bool operator==(const piecewise_constant_distribution& x, const piecewise_constant_distribution& y); // generating functionstemplate<class URBG> result_type operator()(URBG& g); template<class URBG> result_type operator()(URBG& g, const param_type& parm); // property functions vector<result_type> intervals() const;
 vector<result_type> densities() const;
 param_type param() const; void param(const param_type& parm);
 result_type min() const;
 result_type max() const; // inserters and extractorstemplate<class charT, class traits>friend basic_ostream<charT, traits>&operator<<(basic_ostream<charT, traits>& os, const piecewise_constant_distribution& x); template<class charT, class traits>friend basic_istream<charT, traits>&operator>>(basic_istream<charT, traits>& is, piecewise_constant_distribution& x); };}

[🔗](#lib:piecewise_constant_distribution,constructor)

`piecewise_constant_distribution();
`

[3](#pconst-3)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6538)

*Effects*: Constructs a piecewise_constant_distribution object
 with n=1, ρ0=1, b0=0,
 and b1=1[.](#pconst-3.sentence-1)

[🔗](#lib:piecewise_constant_distribution,constructor_)

`template<class InputIteratorB, class InputIteratorW>
  piecewise_constant_distribution(InputIteratorB firstB, InputIteratorB lastB,
                                  InputIteratorW firstW);
`

[4](#pconst-4)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6556)

*Mandates*: Both of

- [(4.1)](#pconst-4.1)

is_convertible_v<iterator_traits<InputIteratorB>​::​value_type, double>

- [(4.2)](#pconst-4.2)

is_convertible_v<iterator_traits<InputIteratorW>​::​value_type, double>

are true[.](#pconst-4.sentence-1)

[5](#pconst-5)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6565)

*Preconditions*: InputIteratorB and InputIteratorW each meet the [*Cpp17InputIterator*](input.iterators#:Cpp17InputIterator "24.3.5.3 Input iterators [input.iterators]") requirements ([[input.iterators]](input.iterators "24.3.5.3 Input iterators"))[.](#pconst-5.sentence-1)

If firstB == lastB or ++firstB == lastB,
 let n=1, w0=1, b0=0,
 and b1=1[.](#pconst-5.sentence-2)

Otherwise, [firstB, lastB) forms a sequence b of length n+1,
 the length of the sequence w starting from firstW is at least n,
 and any wk for k ≥ n are ignored by the distribution[.](#pconst-5.sentence-3)

[6](#pconst-6)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6583)

*Effects*: Constructs a piecewise_constant_distribution object
 with parameters as specified above[.](#pconst-6.sentence-1)

[🔗](#lib:piecewise_constant_distribution,constructor__)

`template<class UnaryOperation>
  piecewise_constant_distribution(initializer_list<RealType> bl, UnaryOperation fw);
`

[7](#pconst-7)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6597)

*Mandates*: is_invocable_r_v<double, UnaryOperation&, double> is true[.](#pconst-7.sentence-1)

[8](#pconst-8)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6601)

*Effects*: Constructs a piecewise_constant_distribution object
 with parameters taken or calculated
 from the following values:
 If bl.size()<2,
 let n=1, w0=1, b0=0,
 and b1=1[.](#pconst-8.sentence-1)

Otherwise,
 let [bl.begin(), bl.end()) form a sequence b0,…,bn,
 and
 let wk=fw((bk+1+bk)/2) for k=0,…,n−1[.](#pconst-8.sentence-2)

[9](#pconst-9)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6618)

*Complexity*: The number of invocations of fw does not exceed n[.](#pconst-9.sentence-1)

[🔗](#lib:piecewise_constant_distribution,constructor___)

`template<class UnaryOperation>
  piecewise_constant_distribution(size_t nw, RealType xmin, RealType xmax, UnaryOperation fw);
`

[10](#pconst-10)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6631)

*Mandates*: is_invocable_r_v<double, UnaryOperation&, double> is true[.](#pconst-10.sentence-1)

[11](#pconst-11)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6635)

*Preconditions*: If nw=0, let n=1, otherwise let n=nw[.](#pconst-11.sentence-1)

The relation 0<δ=(xmax−xmin)/n holds[.](#pconst-11.sentence-2)

[12](#pconst-12)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6641)

*Effects*: Constructs a piecewise_constant_distribution object
 with parameters taken or calculated
 from the following values:
 Let bk=xmin+kâ‹Î´ for k=0,…,n,
 and wk=fw(bk+δ/2) for k=0,…,n−1[.](#pconst-12.sentence-1)

[13](#pconst-13)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6649)

*Complexity*: The number of invocations of fw does not exceed n[.](#pconst-13.sentence-1)

[🔗](#lib:intervals,piecewise_constant_distribution)

`vector<result_type> intervals() const;
`

[14](#pconst-14)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6660)

*Returns*: A vector<result_type> whose size member returns n+1 and whose operator[] member returns bk when invoked with argument k for k=0,…,n[.](#pconst-14.sentence-1)

[🔗](#lib:densities,piecewise_constant_distribution)

`vector<result_type> densities() const;
`

[15](#pconst-15)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6674)

*Returns*: A vector<result_type> whose size member returns n and whose operator[] member returns ρk when invoked with argument k for k=0,…,n−1[.](#pconst-15.sentence-1)

#### [29.5.9.6.3](#plinear) Class template piecewise_linear_distribution [[rand.dist.samp.plinear]](rand.dist.samp.plinear)

[1](#plinear-1)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6690)

A piecewise_linear_distribution random number distribution
produces random numbers x,b0≤x<bn,
distributed over each subinterval[bi,bi+1) according to the probability density function in Formula [29.21](#eq:rand.dist.samp.plinear)[.](#plinear-1.sentence-1)

p(x|b0,…,bn,ρ0,…,ρn)=ρiâ‹bi+1−xbi+1−bi+ρi+1â‹x−bibi+1−bi , for bi≤x<bi+1.(29.21)

[2](#plinear-2)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6704)

The n+1 distribution parameters bi,
also known as this distribution's [*interval boundaries*](#def:interval_boundaries), shall satisfy the relation bi<bi+1 for i=0,…,n−1[.](#plinear-2.sentence-1)

Unless specified otherwise,
the remaining n+1 distribution parameters are calculated asρk=wk/S for k=0,…,n, in which the values wk,
commonly known as the [*weights at boundaries*](#def:weights_at_boundaries), shall be non-negative, non-NaN, and non-infinity[.](#plinear-2.sentence-2)

Moreover, the following relation shall hold:  
0<S=12â‹n−1∑k=0(wk+wk+1)â‹(bk+1−bk) [.](#plinear-2.sentence-3)

[🔗](#lib:piecewise_linear_distribution_)

namespace std {template<class RealType = double>class piecewise_linear_distribution {public:// typesusing result_type = RealType; using param_type = *unspecified*; // constructor and reset functions piecewise_linear_distribution(); template<class InputIteratorB, class InputIteratorW> piecewise_linear_distribution(InputIteratorB firstB, InputIteratorB lastB,
 InputIteratorW firstW); template<class UnaryOperation> piecewise_linear_distribution(initializer_list<RealType> bl, UnaryOperation fw); template<class UnaryOperation> piecewise_linear_distribution(size_t nw, RealType xmin, RealType xmax, UnaryOperation fw); explicit piecewise_linear_distribution(const param_type& parm); void reset(); // equality operatorsfriend bool operator==(const piecewise_linear_distribution& x, const piecewise_linear_distribution& y); // generating functionstemplate<class URBG> result_type operator()(URBG& g); template<class URBG> result_type operator()(URBG& g, const param_type& parm); // property functions vector<result_type> intervals() const;
 vector<result_type> densities() const;
 param_type param() const; void param(const param_type& parm);
 result_type min() const;
 result_type max() const; // inserters and extractorstemplate<class charT, class traits>friend basic_ostream<charT, traits>&operator<<(basic_ostream<charT, traits>& os, const piecewise_linear_distribution& x); template<class charT, class traits>friend basic_istream<charT, traits>&operator>>(basic_istream<charT, traits>& is, piecewise_linear_distribution& x); };}

[🔗](#lib:piecewise_linear_distribution,constructor)

`piecewise_linear_distribution();
`

[3](#plinear-3)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6774)

*Effects*: Constructs a piecewise_linear_distribution object
 with n=1, ρ0=ρ1=1, b0=0,
 and b1=1[.](#plinear-3.sentence-1)

[🔗](#lib:piecewise_linear_distribution,constructor_)

`template<class InputIteratorB, class InputIteratorW>
  piecewise_linear_distribution(InputIteratorB firstB, InputIteratorB lastB,
                                InputIteratorW firstW);
`

[4](#plinear-4)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6791)

*Mandates*: Both of

- [(4.1)](#plinear-4.1)

is_convertible_v<iterator_traits<InputIteratorB>​::​value_type, double>

- [(4.2)](#plinear-4.2)

is_convertible_v<iterator_traits<InputIteratorW>​::​value_type, double>

are true[.](#plinear-4.sentence-1)

[5](#plinear-5)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6800)

*Preconditions*: InputIteratorB and InputIteratorW each meet the [*Cpp17InputIterator*](input.iterators#:Cpp17InputIterator "24.3.5.3 Input iterators [input.iterators]") requirements ([[input.iterators]](input.iterators "24.3.5.3 Input iterators"))[.](#plinear-5.sentence-1)

If firstB == lastB or ++firstB == lastB,
 let n=1, ρ0=ρ1=1, b0=0,
 and b1=1[.](#plinear-5.sentence-2)

Otherwise, [firstB, lastB) forms a sequence b of length n+1,
 the length of the sequence w starting from firstW is at least n+1,
 and any wk for k≥n+1 are ignored by the distribution[.](#plinear-5.sentence-3)

[6](#plinear-6)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6818)

*Effects*: Constructs a piecewise_linear_distribution object
 with parameters as specified above[.](#plinear-6.sentence-1)

[🔗](#lib:piecewise_linear_distribution,constructor__)

`template<class UnaryOperation>
  piecewise_linear_distribution(initializer_list<RealType> bl, UnaryOperation fw);
`

[7](#plinear-7)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6832)

*Mandates*: is_invocable_r_v<double, UnaryOperation&, double> is true[.](#plinear-7.sentence-1)

[8](#plinear-8)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6836)

*Effects*: Constructs a piecewise_linear_distribution object
 with parameters taken or calculated
 from the following values:
 If bl.size()<2,
 let n=1, ρ0=ρ1=1, b0=0,
 and b1=1[.](#plinear-8.sentence-1)

Otherwise,
 let [bl.begin(), bl.end()) form a sequence b0,…,bn,
 and
 let wk=fw(bk) for k=0,…,n[.](#plinear-8.sentence-2)

[9](#plinear-9)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6853)

*Complexity*: The number of invocations of fw does not exceed n+1[.](#plinear-9.sentence-1)

[🔗](#lib:piecewise_linear_distribution,constructor___)

`template<class UnaryOperation>
  piecewise_linear_distribution(size_t nw, RealType xmin, RealType xmax, UnaryOperation fw);
`

[10](#plinear-10)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6866)

*Mandates*: is_invocable_r_v<double, UnaryOperation&, double> is true[.](#plinear-10.sentence-1)

[11](#plinear-11)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6870)

*Preconditions*: If nw=0, let n=1, otherwise let n=nw[.](#plinear-11.sentence-1)

The relation 0<δ=(xmax−xmin)/n holds[.](#plinear-11.sentence-2)

[12](#plinear-12)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6876)

*Effects*: Constructs a piecewise_linear_distribution object
 with parameters taken or calculated
 from the following values:
 Let bk=xmin+kâ‹Î´ for k=0,…,n,
 and wk=fw(bk) for k=0,…,n[.](#plinear-12.sentence-1)

[13](#plinear-13)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6884)

*Complexity*: The number of invocations of fw does not exceed n+1[.](#plinear-13.sentence-1)

[🔗](#lib:intervals,piecewise_linear_distribution)

`vector<result_type> intervals() const;
`

[14](#plinear-14)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6895)

*Returns*: A vector<result_type> whose size member returns n+1 and whose operator[] member returns bk when invoked with argument k for k=0,…,n[.](#plinear-14.sentence-1)

[🔗](#lib:densities,piecewise_linear_distribution)

`vector<result_type> densities() const;
`

[15](#plinear-15)

[#](http://github.com/Eelis/draft/tree/9adde4bc1c62ec234483e63ea3b70a59724c745a/source/numerics.tex#L6909)

*Returns*: A vector<result_type> whose size member returns n and whose operator[] member returns ρk when invoked with argument k for k=0,…,n[.](#plinear-15.sentence-1)