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"""
Select fitted distributions
===========================
"""
# %%
# In this example help to make a choice between several distributions fitted to a sample.
#
# Several methods can be used:
#
# - the ranking by the Kolmogorov p-values (for continuous distributions),
# - the ranking by the ChiSquared p-values (for discrete distributions),
# - the ranking by BIC values.
# %%
import openturns as ot
import openturns.viewer as otv
# %%
# Create a sample from a continuous distribution
distribution = ot.Beta(2.0, 2.0, 0.0, 1.0)
sample = distribution.getSample(1000)
graph = ot.UserDefined(sample).drawCDF()
view = otv.View(graph)
# %%
# **1. Specify the model only**
# %%
# Create the list of distribution estimators
factories = [ot.BetaFactory(), ot.TriangularFactory()]
# %%
# Rank the continuous models by the Lilliefors p-values:
estimated_distribution, test_result = ot.FittingTest.BestModelLilliefors(
sample, factories
)
test_result
# %%
# Rank the continuous models wrt the BIC criteria (no test result):
ot.FittingTest.BestModelBIC(sample, factories)
# %%
# Rank the continuous models wrt the AIC criteria (no test result)
ot.FittingTest.BestModelAIC(sample, factories)
# %%
# Rank the continuous models wrt the AICc criteria (no test result):
ot.FittingTest.BestModelAICC(sample, factories)
# %%
# **2. Specify the model and its parameters**
# %%
# Create a collection of the distributions to be tested
distributions = [ot.Beta(2.0, 2.0, 0.0, 1.0), ot.Triangular(0.0, 0.5, 1.0)]
# %%
# Rank the continuous models by the Kolmogorov p-values:
estimated_distribution, test_result = ot.FittingTest.BestModelKolmogorov(
sample, distributions
)
test_result
# %%
# Rank the continuous models wrt the BIC criteria:
ot.FittingTest.BestModelBIC(sample, distributions)
# %%
# Rank the continuous models wrt the AIC criteria:
ot.FittingTest.BestModelAIC(sample, distributions)
# %%
# Rank the continuous models wrt the AICc criteria:
ot.FittingTest.BestModelAICC(sample, distributions)
# %%
# **Discrete distributions**
# %%
# Create a sample from a discrete distribution
distribution = ot.Poisson(2.0)
sample = distribution.getSample(1000)
graph = ot.UserDefined(sample).drawCDF()
view = otv.View(graph)
# %%
# Create the list of distribution estimators
distributions = [ot.Poisson(2.0), ot.Geometric(0.1)]
# %%
# Rank the discrete models with respect to the ChiSquared p-values:
ot.ResourceMap.SetAsBool("FittingTest-ChiSquaredCheckSample", False)
estimated_distribution, test_result = ot.FittingTest.BestModelChiSquared(
sample, distributions
)
test_result
# %%
# Rank the discrete models with respect to the BIC criteria:
ot.FittingTest.BestModelBIC(sample, distributions)
otv.View.ShowAll()
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