Mixture — SciPy v1.15.3 Manual (original) (raw)
scipy.stats.
class scipy.stats.Mixture(components, *, weights=None)[source]#
Representation of a mixture distribution.
A mixture distribution is the distribution of a random variable defined in the following way: first, a random variable is selected from components according to the probabilities given by weights, then the selected random variable is realized.
Parameters:
componentssequence of ContinuousDistribution
The underlying instances of ContinuousDistribution. All must have scalar shape parameters (if any); e.g., the pdf evaluated at a scalar argument must return a scalar.
weightssequence of floats, optional
The corresponding probabilities of selecting each random variable. Must be non-negative and sum to one. The default behavior is to weight all components equally.
Notes
The following abbreviations are used throughout the documentation.
- PDF: probability density function
- CDF: cumulative distribution function
- CCDF: complementary CDF
- entropy: differential entropy
- log-F: logarithm of F (e.g. log-CDF)
- inverse F: inverse function of F (e.g. inverse CDF)
References
Attributes:
componentssequence of ContinuousDistribution
The underlying instances of ContinuousDistribution.
weightsndarray
The corresponding probabilities of selecting each random variable.
Methods
| support() | Support of the random variable |
|---|---|
| sample([shape, rng, method]) | Random sample from the distribution. |
| moment([order, kind, method]) | Raw, central, or standard moment of positive integer order. |
| mean(*[, method]) | Mean (raw first moment about the origin) |
| median(*[, method]) | Median (50th percentil) |
| mode(*[, method]) | Mode (most likely value) |
| variance(*[, method]) | Variance (central second moment) |
| standard_deviation(*[, method]) | Standard deviation (square root of the second central moment) |
| skewness(*[, method]) | Skewness (standardized third moment) |
| kurtosis(*[, method]) | Kurtosis (standardized fourth moment) |
| pdf(x, /, *[, method]) | Probability density function |
| logpdf(x, /, *[, method]) | Log of the probability density function |
| cdf(x[, y, method]) | Cumulative distribution function |
| icdf(p, /, *[, method]) | Inverse of the cumulative distribution function. |
| ccdf(x[, y, method]) | Complementary cumulative distribution function |
| iccdf(p, /, *[, method]) | Inverse complementary cumulative distribution function. |
| logcdf(x[, y, method]) | Log of the cumulative distribution function |
| ilogcdf(p, /, *[, method]) | Inverse of the logarithm of the cumulative distribution function. |
| logccdf(x[, y, method]) | Log of the complementary cumulative distribution function |
| ilogccdf(p, /, *[, method]) | Inverse of the log of the complementary cumulative distribution function. |
| entropy(*[, method]) | Differential entropy |