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.
|
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.
|
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.
|
dUdx, H, k, setDeltaT
acceptanceRate, nextVector, seed
public 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)
AbstractMetropolis
nextProposedState
in class AbstractMetropolis
currentState
- the current state of the systemprotected boolean isProposalAccepted(Vector currentState, Vector proposedState)
AbstractMetropolis
isProposalAccepted
in class AbstractMetropolis
currentState
- the current state of the systemproposedState
- the proposed next state of the systemCopyright © 2010-2020 NM FinTech Ltd.. All Rights Reserved.