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Constraining uncertain values

constrain

# UncertainData.SamplingConstraints.constrain — Method.

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constrain(uv::AbstractUncertainValue, constraint::SamplingConstraint)

Apply the constraint and truncate the support of the distribution furnishing the uncertain value uv. Returns a constrained uncertain value.

source

Examples

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using UncertainData, Distributions

# Define an uncertain value furnished by a theoretical distribution
uv = UncertainValue(Normal, 1, 0.5)

# Constrain the support of the furnishing distribution using various
# constraints
uvc_lq = constrain(uv, TruncateLowerQuantile(0.2))
uvc_uq = constrain(uv, TruncateUpperQuantile(0.8))
uvc_q = constrain(uv, TruncateQuantiles(0.2, 0.8))
uvc_min = constrain(uv, TruncateMinimum(0.5))
uvc_max = constrain(uv, TruncateMaximum(1.5))
uvc_range = constrain(uv, TruncateRange(0.5, 1.5))

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using UncertainData, Distributions

# Define an uncertain value furnished by a theoretical distribution with
# parameters fitted to empirical data
uv = UncertainValue(Normal, rand(Normal(-1, 0.2), 1000))

# Constrain the support of the furnishing distribution using various
# constraints
uvc_lq = constrain(uv, TruncateLowerQuantile(0.2))
uvc_uq = constrain(uv, TruncateUpperQuantile(0.8))
uvc_q = constrain(uv, TruncateQuantiles(0.2, 0.8))
uvc_min = constrain(uv, TruncateMinimum(0.5))
uvc_max = constrain(uv, TruncateMaximum(1.5))
uvc_range = constrain(uv, TruncateRange(0.5, 1.5))

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# Define an uncertain value furnished by a kernel density estimate to the
# distribution of the empirical data
uv = UncertainValue(UnivariateKDE, rand(Uniform(10, 15), 1000))

# Constrain the support of the furnishing distribution using various
# constraints
uvc_lq = constrain(uv, TruncateLowerQuantile(0.2))
uvc_uq = constrain(uv, TruncateUpperQuantile(0.8))
uvc_q = constrain(uv, TruncateQuantiles(0.2, 0.8))
uvc_min = constrain(uv, TruncateMinimum(13))
uvc_max = constrain(uv, TruncateMaximum(13))
uvc_range = constrain(uv, TruncateRange(11, 12))