| Safe Haskell | None |
|---|---|
| Language | Haskell2010 |
MCMC.Types
- type Rand = GenIO
- type Density a = a -> Double
- type Sample a = Rand -> IO a
- data Target a
- viewTarget :: Target a -> TargetView a
- newtype TargetView a = Target (Density a)
- makeTarget :: Density a -> Target a
- data Proposal a
- viewProposal :: Proposal a -> ProposalView a
- data ProposalView a = Proposal (Density a) (Sample a)
- makeProposal :: Density a -> Sample a -> Proposal a
- type Step x = Rand -> x -> IO x
- type Kernel x a = Target a -> (a -> Proposal a) -> Step x
- type Act x m a = x -> a -> m a
- data Action x m a b
- viewAction :: Action x m a b -> ActionView x m a b
- data ActionView x m a b = Action (Act x m a) (a -> m b) a
- makeAction :: Act x m a -> (a -> m b) -> a -> Action x m a b
Targets and Proposals
The probability density function used in both target and proposal distributions. Given an input point, this method returns a probability density.
A procedure that, given a source of randomness, returns an action that produces a sample. The type itself is read as a verb, i.e, "to sample".
data Target a
The type for target distributions that can be used in any MCMC sampler.
Instances
| HasDensity Target a | Get the probability density at a point for any target distribution. |
viewTarget :: Target a -> TargetView a
newtype TargetView a
makeTarget :: Density a -> Target a
Method for constructing custom target distributions.
Target distributions need only a density method.
data Proposal a
The type for proposal distributions that can be used in any MCMC sampler.
Instances
| HasDensity Proposal a | Get the probability density at a point for any proposal distribution. |
viewProposal :: Proposal a -> ProposalView a
data ProposalView a
makeProposal :: Density a -> Sample a -> Proposal a
Method for constructing custom proposal distributions.
Proposal distributions need both a density and a sampling method.
Transition kernels
type Step x = Rand -> x -> IO x
The type for one step in the random walk.
A value of type Step a
type Kernel x a = Target a -> (a -> Proposal a) -> Step x
The type for MCMC transition kernels.
The input arguments are the target distribution (to be modeled) and a conditional proposal distribution.
The result is a Step that will make one move in the random walk based
on the current state.
In general, an MCMC kernel consists of using:
- the conditioned proposal to make a hypothesis move, and then
- the semantics of the specific
Kernelat hand to either accept this hypothesis (and move to the new state), or reject the hypothesis (and stay at the current state).
Type parameter definitions:
x- The kernel-state. This is the type for each state in the Markov chain.
a- The distribution-state. This is the domain of the target distribution as well as the type of values sampled from the proposal distribution.
In general, we need different types to represent the kernel-state and
distribution-state because the
kernel-state may hold extra information that gets updated with each step.
Look at simulatedAnnealing for
an example where x differs from a.
Actions
type Act x m a = x -> a -> m a
data Action x m a b
Type parameter definitions:
x- The kernel-state (see
Kernel) a- The action-state, specific to the action being performed
m- The monad in which the action is performed
b- The final returned state type
viewAction :: Action x m a b -> ActionView x m a b
data ActionView x m a b