public class RobustAdaptiveMetropolis extends AbstractMetropolis
| Constructor and Description |
|---|
RobustAdaptiveMetropolis(RealScalarFunction logf,
double targetAcceptance,
Vector initialState,
RandomLongGenerator uniform)
Constructs an instance which assumes an initial variance of 1 per variable, uses a gamma of
0.5.
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RobustAdaptiveMetropolis(RealScalarFunction logf,
Matrix initialScale,
double gamma,
double targetAcceptance,
Vector initialState,
RandomStandardNormalGenerator rnorm,
RandomLongGenerator uniform)
Constructs a new instance with the given parameters.
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| Modifier and Type | Method and Description |
|---|---|
protected boolean |
isProposalAccepted(Vector currentState,
Vector proposedState)
Decides whether the given proposed state should be accepted, or whether the system should
remain in it's current state.
|
protected Vector |
nextProposedState(Vector currentState)
Proposes a next state for the system.
|
Matrix |
S()
Gets the tuned scaling matrix (this changes each time a new sample is drawn).
|
acceptanceRate, nextVector, seedpublic RobustAdaptiveMetropolis(RealScalarFunction logf, Matrix initialScale, double gamma, double targetAcceptance, Vector initialState, RandomStandardNormalGenerator rnorm, RandomLongGenerator uniform)
logf - the log of the unnormalized pdf from which we wish to sampleinitialScale - the initial scale matrix, the square root of the covariance matrix,
applied to the vector of independently distributed Normal variables.
This must be a lower triangular matrix with positive diagonal elements.gamma - a value between 0.5 and 1, that controls the speed of adaption. A
lower gamma will lead to faster adoption.targetAcceptance - the target acceptance rateinitialState - the initial state of the algorithmrnorm - the random standard Normal generator to be useduniform - the random long generator to be usedpublic RobustAdaptiveMetropolis(RealScalarFunction logf, double targetAcceptance, Vector initialState, RandomLongGenerator uniform)
logf - the log of the unnormalized pdf from which we wish to sampletargetAcceptance - the target acceptance rateinitialState - the initial state of the algorithmuniform - the random long generator to be usedprotected Vector nextProposedState(Vector currentState)
AbstractMetropolisnextProposedState in class AbstractMetropoliscurrentState - the current state of the systemprotected boolean isProposalAccepted(Vector currentState, Vector proposedState)
AbstractMetropolisisProposalAccepted in class AbstractMetropoliscurrentState - the current state of the systemproposedState - the proposed next state of the systempublic Matrix S()
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