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- Probability.Probability
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- BetaDist
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- FRatio
- Order
- StudentT
class BetaDist(Probability.Probability) |
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Methods defined here:
- Density(self, x)
- Distribution(self, x)
- InverseDistribution(self, y, xEpsilon=9.9999999999999995e-08)
- __init__(self, xA, xB)
- __repr__(self)
Methods inherited from Probability.Probability:
- DensityTest(self, xX, **dArgs)
- Compare the Density() with the numerically differentiated Distribution().
This doesn't make much sense in the abstract base class. But in general, the Density() method will be redefined, and this will be a real test.
- InverseTest(self, xY, **dArgs)
- Run a y value through InverseDistribution and then through Distribution, and compare the results.
- Sample(self)
- Return a random sample distributed according to the probability law.
By default, pass a uniform random sample to InverseDistribution().
Similarly, passing a sample through Distribution() will give you a uniform sample on [0, 1].
- __str__(self)
- getIQR(self)
- getMean(self)
- getMedian(self)
- getStDev(self)
- getVariance(self)
- html(self)
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class FRatio(BetaDist) |
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Fisher's F distribution as a variant on the Beta distribution
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- Method resolution order:
- FRatio
- BetaDist
- Probability.Probability
Methods defined here:
- Density(self, x)
- Distribution(self, x)
- InverseDistribution(self, y, xEpsilon=9.9999999999999995e-08)
- __init__(self, nM, nN)
Methods inherited from BetaDist:
- __repr__(self)
Methods inherited from Probability.Probability:
- DensityTest(self, xX, **dArgs)
- Compare the Density() with the numerically differentiated Distribution().
This doesn't make much sense in the abstract base class. But in general, the Density() method will be redefined, and this will be a real test.
- InverseTest(self, xY, **dArgs)
- Run a y value through InverseDistribution and then through Distribution, and compare the results.
- Sample(self)
- Return a random sample distributed according to the probability law.
By default, pass a uniform random sample to InverseDistribution().
Similarly, passing a sample through Distribution() will give you a uniform sample on [0, 1].
- __str__(self)
- getIQR(self)
- getMean(self)
- getMedian(self)
- getStDev(self)
- getVariance(self)
- html(self)
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class Order(BetaDist) |
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Given a base probability law governing a random sample of size n, this is the probability governing the r-th largest member of that sample, for r = 1, ..., n.
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- Method resolution order:
- Order
- BetaDist
- Probability.Probability
Methods defined here:
- Density(self, x)
- Distribution(self, x)
- Sample(self)
- __init__(self, prob, nR, nN)
- __repr__(self)
- __str__(self)
Methods inherited from BetaDist:
- InverseDistribution(self, y, xEpsilon=9.9999999999999995e-08)
Methods inherited from Probability.Probability:
- DensityTest(self, xX, **dArgs)
- Compare the Density() with the numerically differentiated Distribution().
This doesn't make much sense in the abstract base class. But in general, the Density() method will be redefined, and this will be a real test.
- InverseTest(self, xY, **dArgs)
- Run a y value through InverseDistribution and then through Distribution, and compare the results.
- getIQR(self)
- getMean(self)
- getMedian(self)
- getStDev(self)
- getVariance(self)
- html(self)
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class StudentT(BetaDist) |
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Student's t distribution as a variant on the Beta distribution
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- Method resolution order:
- StudentT
- BetaDist
- Probability.Probability
Methods defined here:
- Density(self, x)
- Distribution(self, x)
- InverseDistribution(self, y, xEpsilon=9.9999999999999995e-08)
- __init__(self, nDF)
- __repr__(self)
Methods inherited from Probability.Probability:
- DensityTest(self, xX, **dArgs)
- Compare the Density() with the numerically differentiated Distribution().
This doesn't make much sense in the abstract base class. But in general, the Density() method will be redefined, and this will be a real test.
- InverseTest(self, xY, **dArgs)
- Run a y value through InverseDistribution and then through Distribution, and compare the results.
- Sample(self)
- Return a random sample distributed according to the probability law.
By default, pass a uniform random sample to InverseDistribution().
Similarly, passing a sample through Distribution() will give you a uniform sample on [0, 1].
- __str__(self)
- getIQR(self)
- getMean(self)
- getMedian(self)
- getStDev(self)
- getVariance(self)
- html(self)
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