Metropolis Samplers
These samplers include a Metropolis-Hastings style step that ensure that the Boltzmann distribution $\mu(x) \propto e^{-\beta V(x)}$ is exactly targeted; there is no bias associated with, for instance, a finite time step Δt.
Zeroth Order Methods
These are samplers which do not require the gradienet of the potential, ∇V.
BasicMD.RWM
— MethodRWM(V, β, Δt)
Set up the RWM sampler for Boltzmann.
Fields
- V - Potential
- β - Inverse temperature
- Δt - Time step
First Order Methods
These are samplers which require the gradienet of the potential, ∇V, and are in the spirit of first order in time discretizations.
BasicMD.MALA
— MethodMALA(V, ∇V!, β, Δt)
Set up the MALA sampler for overdamped Langevin.
Fields
- V - Potential
- ∇V! - In place gradient of the potential
- β - Inverse temperature
- Δt - Time step
Second Order Methods
These are samplers which require the gradienet of the potential, ∇V, and are in the spirit of second order in time discretizations.
BasicMD.HMC
— MethodHMC(V, ∇V!, β, M, Δt, nΔt)
Set up the HMC sampler for Boltzmann.
Fields
- V - Potential
- ∇V! - In place gradient of the potential
- β - Inverse temperature
- M - Mass matrix
- Δt - Time step
- nΔt - Number of time steps to use in each Verlet run