| Safe Haskell | None |
|---|---|
| Language | Haskell2010 |
Goal.Probability.Conditional
Description
Statistical models where the observations depend on known conditions.
Synopsis
- type SampleMap z x = Map (SamplePoint x) (Sample z)
- (>.>*) :: (Map Natural f y x, ExponentialFamily x) => (Natural # f y x) -> SamplePoint x -> Natural # y
- (>$>*) :: (Map Natural f y x, ExponentialFamily x) => (Natural # f y x) -> Sample x -> [Natural # y]
- (*<.<) :: (Map Natural f x y, Bilinear f y x, ExponentialFamily y) => SamplePoint y -> (Natural # f y x) -> Natural # x
- (*<$<) :: (Map Natural f x y, Bilinear f y x, ExponentialFamily y) => Sample y -> (Natural # f y x) -> [Natural # x]
- conditionalLogLikelihood :: (ExponentialFamily x, Map Natural f y x, LogLikelihood Natural y t) => [(t, SamplePoint x)] -> (Natural # f y x) -> Double
- conditionalLogLikelihoodDifferential :: (ExponentialFamily x, LogLikelihood Natural y t, Propagate Natural f y x) => [(t, SamplePoint x)] -> (Natural # f y x) -> Mean # f y x
- conditionalDataMap :: Ord x => [(t, x)] -> Map x [t]
- kFoldMap :: Ord x => Int -> Map x [y] -> [(Map x [y], Map x [y])]
- kFoldMap' :: Ord x => Int -> Map x [y] -> [(Map x [y], Map x [y], Map x [y])]
- mapConditionalLogLikelihood :: (ExponentialFamily x, Map Natural f y x, LogLikelihood Natural y t) => Map (SamplePoint x) [t] -> (Natural # f y x) -> Double
- mapConditionalLogLikelihoodDifferential :: (ExponentialFamily x, LogLikelihood Natural y t, Propagate Natural f y x, Ord (SamplePoint x)) => Map (SamplePoint x) [t] -> (Natural # f y x) -> Mean # f y x
- parMapConditionalLogLikelihood :: (ExponentialFamily x, Map Natural f y x, LogLikelihood Natural y t) => Map (SamplePoint x) [t] -> (Natural # f y x) -> Double
- parMapConditionalLogLikelihoodDifferential :: (ExponentialFamily x, LogLikelihood Natural y t, Propagate Natural f y x, Ord (SamplePoint x)) => Map (SamplePoint x) [t] -> (Natural # f y x) -> Mean # f y x
Documentation
type SampleMap z x = Map (SamplePoint x) (Sample z) Source #
A synonym for Maps from Inputs to Outputs that matches the confusing, backwards style of Goal.
Markov Kernels
(>.>*) :: (Map Natural f y x, ExponentialFamily x) => (Natural # f y x) -> SamplePoint x -> Natural # y infix 8 Source #
Evalutes the given conditional distribution at a SamplePoint.
(>$>*) :: (Map Natural f y x, ExponentialFamily x) => (Natural # f y x) -> Sample x -> [Natural # y] infix 8 Source #
Mapped application of conditional distributions on a Sample.
(*<.<) :: (Map Natural f x y, Bilinear f y x, ExponentialFamily y) => SamplePoint y -> (Natural # f y x) -> Natural # x infix 8 Source #
Applies the transpose of a Bilinear Map to a SamplePoint.
(*<$<) :: (Map Natural f x y, Bilinear f y x, ExponentialFamily y) => Sample y -> (Natural # f y x) -> [Natural # x] infix 8 Source #
Mapped transpose application on a Sample.
Conditional Distributions
conditionalLogLikelihood Source #
Arguments
| :: (ExponentialFamily x, Map Natural f y x, LogLikelihood Natural y t) | |
| => [(t, SamplePoint x)] | Output/Input Pairs |
| -> (Natural # f y x) | Function |
| -> Double | conditional cross entropy estimate |
The conditional logLikelihood for a conditional distribution.
conditionalLogLikelihoodDifferential Source #
Arguments
| :: (ExponentialFamily x, LogLikelihood Natural y t, Propagate Natural f y x) | |
| => [(t, SamplePoint x)] | Output/Input Pairs |
| -> (Natural # f y x) | Function |
| -> Mean # f y x | Differential |
The conditional logLikelihoodDifferential for a conditional distribution.
Turns a list of input/output pairs into a Map, by collecting into lists the different outputs to each particular input.
kFoldMap :: Ord x => Int -> Map x [y] -> [(Map x [y], Map x [y])] Source #
Partition a conditional dataset into k > 1 (training,validation) pairs, where each dataset condition is partitioned to match its size.
kFoldMap' :: Ord x => Int -> Map x [y] -> [(Map x [y], Map x [y], Map x [y])] Source #
Partition a conditional dataset into k > 2 (training,test,validation) triplets, where each dataset condition is partitioned to match its size.
mapConditionalLogLikelihood Source #
Arguments
| :: (ExponentialFamily x, Map Natural f y x, LogLikelihood Natural y t) | |
| => Map (SamplePoint x) [t] | Output/Input Pairs |
| -> (Natural # f y x) | Function |
| -> Double | conditional cross entropy estimate |
The conditional logLikelihood for a conditional distribution, where
redundant conditions/inputs are combined. This can dramatically increase performance when
the number of distinct conditions/inputs is small.
mapConditionalLogLikelihoodDifferential Source #
Arguments
| :: (ExponentialFamily x, LogLikelihood Natural y t, Propagate Natural f y x, Ord (SamplePoint x)) | |
| => Map (SamplePoint x) [t] | Output/Input Pairs |
| -> (Natural # f y x) | Function |
| -> Mean # f y x | Differential |
The conditional logLikelihoodDifferential, where redundant conditions are
combined. This can dramatically increase performance when the number of
distinct conditions is small.
parMapConditionalLogLikelihood Source #
Arguments
| :: (ExponentialFamily x, Map Natural f y x, LogLikelihood Natural y t) | |
| => Map (SamplePoint x) [t] | Output/Input Pairs |
| -> (Natural # f y x) | Function |
| -> Double | conditional cross entropy estimate |
The conditional logLikelihood for a conditional distribution, where
redundant conditions/inputs are combined. This can dramatically increase performance when
the number of distinct conditions/inputs is small.
parMapConditionalLogLikelihoodDifferential Source #
Arguments
| :: (ExponentialFamily x, LogLikelihood Natural y t, Propagate Natural f y x, Ord (SamplePoint x)) | |
| => Map (SamplePoint x) [t] | Output/Input Pairs |
| -> (Natural # f y x) | Function |
| -> Mean # f y x | Differential |
The conditional logLikelihoodDifferential, where redundant conditions are
combined. This can dramatically increase performance when the number of
distinct conditions is small.