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The ti(cauchy_distribution<RealType = double>) looks similar to a normal
distribution. But cauchy distributions have heavier tails. When studying
hypothesis tests that assume normality, seeing how the tests perform on data
from a Cauchy distribution is a good indicator of how sensitive the tests are
to heavy-tail departures from normality.
The mean and standard deviation of the Cauchy distribution are undefined.
Defined types:
verb( using result_type = RealType;
struct param_type
{
explicit param_type(RealType a = RealType(0),
RealType b = RealType(1));
double a() const;
double b() const;
};)
Constructors and members:
itemization(
itt(cauchy_distribution<>(RealType a = RealType(0),
RealType b = RealType(1)))
constructs a cauchy distribution with specified tt(a) and tt(b)
parameters.
itt(cauchy_distribution<>(param_type const ¶m))
constructs a cauchy distribution according to the values stored in
the tt(param) struct.
itt(RealType a() const)nl()
returns the distribution's tt(a) parameter;
itt(RealType b() const)nl()
returns the distribution's tt(b) parameter;
itt(result_type min() const)nl()
returns the smallest positive tt(result_type) value;
itt(result_type max() const)nl()
returns the maximum value of tt(result_type);
)
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