public class MultipointHybridMCMC extends AbstractHybridMCMC
dt| Constructor and Description |
|---|
MultipointHybridMCMC(RealScalarFunction logF,
RealVectorFunction dLogF,
Vector m,
double dt,
int L,
int M,
Vector initialState,
RandomLongGenerator uniform)
Constructs a new instance with equal weights to the M configurations.
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MultipointHybridMCMC(RealScalarFunction logF,
RealVectorFunction dLogF,
Vector m,
double dt,
int L,
int M,
Vector w,
Vector initialState,
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.
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protected Vector |
nextProposedState(Vector currentState)
Proposes a next state for the system.
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dUdx, H, k, setDeltaTacceptanceRate, nextVector, seedpublic MultipointHybridMCMC(RealScalarFunction logF, RealVectorFunction dLogF, Vector m, double dt, int L, int M, Vector w, Vector initialState, RandomLongGenerator uniform)
logF - the log of the unnormalized target density from which we wish to sampledLogF - the derivative of the log target density for use by the
LeapFrogging algorithm. You may choose a function that differs
from the actual derivative of the log target density (i.e. that of a tempered version of the
target density), in order to guide the leap-frogging algorithmm - the mass of each component in the dynamics simulation. A lower mass for a
given component will result in greater change over the simulated timedt - the amount by which we advance time at each dynamics simulation stepL - the number of dynamics simulation stepsM - the number of configurations from which we select the candidate after the
forward leap-frog iterations (M < L)w - a vector of length M, which is used to emphasize certain steps along the
leapfrog trajectory, where w(1) is the weight assigned to the to configuration at step L -
M and w(M) corresponds to the configuration at step L.initialState - the initial state of the algorithmuniform - the random long generator to be usedpublic MultipointHybridMCMC(RealScalarFunction logF, RealVectorFunction dLogF, Vector m, double dt, int L, int M, Vector initialState, RandomLongGenerator uniform)
logF - the log of the unnormalized target density from which we wish to sampledLogF - the derivative of the log target density for use by the
LeapFrogging algorithm. You may choose a function that differs
from the actual derivative of the log target density (i.e. that of a tempered version of the
target density), in order to guide the leap-frogging algorithmm - the mass of each component in the dynamics simulation. A lower mass for a
given component will result in greater change over the simulated timedt - the difference in time for each simulation step. A smaller value, will
make the simulation more accurate, but a larger value will give better performance by
requiring less simulation stepsL - the number of forward leap-frog iterations at each stepM - the number of configurations from which we select the candidate after the
forward leap-frog iterations (M < L)initialState - 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 systemCopyright © 2010-2020 NM FinTech Ltd.. All Rights Reserved.