|
| | FisherFDistribution () |
| | Default constructor. Creates F(1, 1) distribution.
|
| |
| | FisherFDistribution (RealType m, RealType n=RealType(1)) |
| | Constructs an F-distribution with specified degrees of freedom.
|
| |
| | FisherFDistribution (const param_type ¶m) |
| | Constructs from a parameter set.
|
| |
| void | reset () |
| | Resets the distribution state.
|
| |
| RealType | m () const |
| | Gets the numerator degrees of freedom.
|
| |
| RealType | n () const |
| | Gets the denominator degrees of freedom.
|
| |
| param_type | param () const |
| | Gets the current parameter set.
|
| |
| void | param (const param_type ¶m) |
| | Sets new parameters for the distribution.
|
| |
| result_type | min () const |
| | Gets the minimum value that can be generated (approaches 0).
|
| |
| result_type | max () const |
| | Gets the theoretical maximum value (positive infinity).
|
| |
| template<typename Generator > |
| result_type | operator() (Generator &g) |
| | Generates the next random value using stored parameters.
|
| |
| template<typename Generator > |
| result_type | operator() (Generator &g, const param_type ¶m) |
| | Generates the next random value using provided parameters.
|
| |
template<typename RealType = double>
class Infinity::Engine::FisherFDistribution< RealType >
Platform-independent Fisher F-distribution.
Produces values according to the F-distribution with m and n degrees of freedom. The F-distribution arises naturally when comparing two chi-squared distributions, making it essential for analysis of variance (ANOVA) and comparing variances.
If X ~ ChiSquared(m) and Y ~ ChiSquared(n), then F = (X/m)/(Y/n) ~ F(m, n). This distribution is fundamental in statistical hypothesis testing, particularly for testing equality of variances and in regression analysis.
- Template Parameters
-
| RealType | Floating point type (float or double) |
Example usage:
float value = f_dist(rng);
for (int i = 0; i < 1000; ++i) {
double f_value = anova_stat(rng);
}
Platform-independent Fisher F-distribution.
Definition PRNGDistribution.hpp:1525
Pseudo-random number generator for procedural generation.
Definition PRNG.hpp:48
- Note
- All values are strictly positive.
-
Mean = n/(n-2) for n > 2; undefined for n ≤ 2.
-
Variance = 2n²(m+n-2) / [m(n-2)²(n-4)] for n > 4.
-
As both m and n increase, the distribution becomes more symmetric.