boost/math
2
0
Fork 0
mirror of https://github.com/boostorg/math.git synced 2026-01-19 04:22:09 +00:00
Code Issues Projects Releases Wiki Activity

Add notes to individual dist docs for specializations

Browse Source
This commit is contained in:
Matt Borland
2024-08-12 14:11:43 -04:00
parent 0d7c944752
commit d892e03070
12 changed files with 36 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
doc/distributions/exponential.qbk
View File

@@ -57,6 +57,9 @@ that are generic to all distributions are supported: __usual_accessors.
The domain of the random variable is \[0, +[infin]\].
In this distribution the implementation of both `logcdf`, and `logpdf` are specialized
to improve numerical accuracy.
[h4 Accuracy]
The exponential distribution is implemented in terms of the

3
doc/distributions/extreme_value.qbk
View File

@@ -81,6 +81,9 @@ that are generic to all distributions are supported: __usual_accessors.
The domain of the random parameter is \[-[infin], +[infin]\].
In this distribution the implementation of both `logcdf`, and `logpdf` are specialized
to improve numerical accuracy.
[h4 Accuracy]
The extreme value distribution is implemented in terms of the

3
doc/distributions/gamma.qbk
View File

@@ -99,6 +99,9 @@ distributions are supported: __usual_accessors.
The domain of the random variable is \[0,+[infin]\].
In this distribution the implementation of `logpdf` is specialized
to improve numerical accuracy.
[h4 Accuracy]
The gamma distribution is implemented in terms of the

3
doc/distributions/geometric.qbk
View File

@@ -303,6 +303,9 @@ the context of this distribution:
``quantile(complement(geometric(p), P))``]]
]
In this distribution the implementation of `logcdf` is specialized
to improve numerical accuracy.
[h4 Accuracy]
This distribution is implemented using the pow and exp functions, so most results

3
doc/distributions/inverse_gamma.qbk
View File

@@ -87,6 +87,9 @@ The domain of the random variate is \[0,+[infin]\].
[note Unlike some definitions, this implementation supports a random variate
equal to zero as a special case, returning zero for pdf and cdf.]
In this distribution the implementation of `logpdf` is specialized
to improve numerical accuracy.
[h4 Accuracy]
The inverse gamma distribution is implemented in terms of the

3
doc/distributions/inverse_gaussian.qbk
View File

@@ -115,6 +115,9 @@ The domain of the random variate is \[0,+[infin]).
[note Unlike some definitions, this implementation supports a random variate
equal to zero as a special case, returning zero for both pdf and cdf.]
In this distribution the implementation of `logpdf` is specialized
to improve numerical accuracy.
[h4 Accuracy]
The inverse_gaussian distribution is implemented in terms of the

3
doc/distributions/laplace.qbk
View File

@@ -76,6 +76,9 @@ distributions are supported: __usual_accessors.
The domain of the random variable is \[-[infin],+[infin]\].
In this distribution the implementation of both `logcdf`, and `logpdf` are specialized
to improve numerical accuracy.
[h4 Accuracy]
The laplace distribution is implemented in terms of the

3
doc/distributions/logistic.qbk
View File

@@ -67,6 +67,9 @@ At `p=1` and `p=0`, the quantile function returns the result of
quantile function returns the result of -__overflow_error and
+__overflow_error respectively.
In this distribution the implementation of `logcdf` is specialized
to improve numerical accuracy.
[h4 Accuracy]
The logistic distribution is implemented in terms of the `std::exp`

3
doc/distributions/pareto.qbk
View File

@@ -73,6 +73,9 @@ distributions are supported: __usual_accessors.
The supported domain of the random variable is \[scale, [infin]\].
In this distribution the implementation of `logcdf` is specialized
to improve numerical accuracy.
[h4 Accuracy]
The Pareto distribution is implemented in terms of the

3
doc/distributions/poisson.qbk
View File

@@ -62,6 +62,9 @@ distributions are supported: __usual_accessors.
The domain of the random variable is \[0, [infin]\].
In this distribution the implementation of `logpdf` is specialized
to improve numerical accuracy.
[h4 Accuracy]
The Poisson distribution is implemented in terms of the

3
doc/distributions/rayleigh.qbk
View File

@@ -77,6 +77,9 @@ distributions are supported: __usual_accessors.
The domain of the random variable is \[0, max_value\].
In this distribution the implementation of both `logcdf`, and `logpdf` are specialized
to improve numerical accuracy.
[h4 Accuracy]
The Rayleigh distribution is implemented in terms of the

3
doc/distributions/weibull.qbk
View File

@@ -88,6 +88,9 @@ distributions are supported: __usual_accessors.
The domain of the random variable is \[0, [infin]\].
In this distribution the implementation of both `logcdf`, and `logpdf` are specialized
to improve numerical accuracy.
[h4 Accuracy]
The Weibull distribution is implemented in terms of the