org.omegahat.Simulation.MCMC
Interface HastingsCoupledProposal

All Known Implementing Classes:
AdaptiveMultiKernelProposal, AdaptiveProposal, HastingsCoupledIteratedProposal, HastingsCoupledMixtureProposal, IndependentHastingsCoupledProposal, KernelDirectionSampler, KernelProposal

public interface HastingsCoupledProposal

This class extends the GeneralProposal interface by adding methods appropriate for generating states for a HastingsCoupledSampler. This interface replaces to AdaptiveProposal, which is now depreciated.


Method Summary
 double conditionalPDF(java.lang.Object state, java.lang.Object conditions, int which, MultiState stateVector)
          Computes the HastingsCoupled transition probability for one sub-state given the entire state vector
 java.lang.Object generate(java.lang.Object conditions, int which, MultiState stateVector)
          Generate a new component state given the entire state vector
 double logConditionalPDF(java.lang.Object state, java.lang.Object conditions, int which, MultiState stateVector)
          Computes the HastingsCoupled transition probability for one sub-state given the entire state vector
 double logTransitionProbability(java.lang.Object from, java.lang.Object to, int which, MultiState stateVector)
          Computes the HastingsCoupled transition probability for one sub-state given the entire state vector
 double transitionProbability(java.lang.Object from, java.lang.Object to, int which, MultiState stateVector)
          Computes the HastingsCoupled transition probability for one sub-state given the entire state vector
 

Method Detail

conditionalPDF

public double conditionalPDF(java.lang.Object state,
                             java.lang.Object conditions,
                             int which,
                             MultiState stateVector)
Computes the HastingsCoupled transition probability for one sub-state given the entire state vector
Parameters:
conditions - Current value of the component state being updated
state - Proposed state of the component state being updated
which - index of the component state being updated
stateVector - entire set of states, including the state being updated

logConditionalPDF

public double logConditionalPDF(java.lang.Object state,
                                java.lang.Object conditions,
                                int which,
                                MultiState stateVector)
Computes the HastingsCoupled transition probability for one sub-state given the entire state vector
Parameters:
conditions - Current value of the component state being updated
state - Proposed state of the component state being updated
which - index of the component state being updated
stateVector - entire set of states, including the state being updated

generate

public java.lang.Object generate(java.lang.Object conditions,
                                 int which,
                                 MultiState stateVector)
Generate a new component state given the entire state vector
Parameters:
conditions - Current value of the component state being updated
which - index of the component state being updated
stateVector - entire set of states, including the state being updated

transitionProbability

public double transitionProbability(java.lang.Object from,
                                    java.lang.Object to,
                                    int which,
                                    MultiState stateVector)
Computes the HastingsCoupled transition probability for one sub-state given the entire state vector
Parameters:
from - Current value of the component state being updated
to - Proposed state of the component state being updated
which - index of the component state being updated
stateVector - entire set of states, including the state being updated

logTransitionProbability

public double logTransitionProbability(java.lang.Object from,
                                       java.lang.Object to,
                                       int which,
                                       MultiState stateVector)
Computes the HastingsCoupled transition probability for one sub-state given the entire state vector
Parameters:
from - Current value of the component state being updated
to - Proposed state of the component state being updated
which - index of the component state being updated
stateVector - entire set of states, including the state being updated