diff --git a/src/trajectory.jl b/src/trajectory.jl
index 066fcc8d..2e73a192 100644
--- a/src/trajectory.jl
+++ b/src/trajectory.jl
@@ -221,7 +221,7 @@ function accept_phasepoint!(z::T, z′::T, is_accept) where {T<:PhasePoint{<:Abs
     return z′
 end
 
-### Use end-point from trajecory as proposal 
+### Use end-point from trajecory as proposal
 
 samplecand(rng, τ::StaticTrajectory{EndPointTS}, h, z) = step(τ.integrator, h, z, τ.n_steps)
 
@@ -706,3 +706,108 @@ function mh_accept_ratio(
     accept = rand(rng, T, length(Horiginal)) .< α
     return accept, α
 end
+
+##
+## Stochastic Gradient Hamilton Samplers
+##
+
+###
+### Stochastic Gradient Hamiltonian Monte Carlo sampler.
+###
+"""
+Stochastic Gradient HMC with fixed number of steps.
+"""
+struct SGHMC{
+    I<:AbstractIntegrator,
+    F<:AbstractFloat
+} <: AbstractTrajectory{I}
+    integrator      :: I
+    n_steps         :: Int  # number of samples
+    batch_size      :: Int  # no of data points in minibatch for gradient estimate
+    η               :: F    # learning rate
+    α               :: F    # momentum decay
+end
+
+function transition(
+    rng::AbstractRNG,
+    τ::SGHMC,
+    h::Hamiltonian,
+    z::PhasePoint
+) where {T<:Real}
+    # z′ = step(rng, τ.integrator, h, z, τ.n_steps)
+
+    m, η, α, D = τ.n_steps, τ.η, τ.α, τ.batch_size
+    
+    @unpack θ, r = z
+
+    for i=1:m
+        # ToDo: how to compute stochastic gradient
+        stoch_grad = gradient(h, D)
+        
+        # update position
+        θ .+= r
+
+        # update momentum
+        r .= (1 - α) .* r .+ η .* stoch_grad .+ rand.(Normal.(zeros(length(θ)), sqrt(2 * η * α)))
+    end
+
+    # no M-H step
+    z = PhasePoint(θ, r, z′.ℓπ, z′.ℓκ)
+    stat = (
+        step_size=τ.integrator.ϵ,
+        n_steps=τ.n_steps,
+        log_density=z.ℓπ.value,
+        hamiltonian_energy=energy(z),
+        )
+    return Transition(z, stat)
+end
+
+
+
+###
+### Stochastic Gradient Langevin Dynamics sampler.
+###
+"""
+Stochastic Gradient Langevin Dynamics with fixed number of steps.
+"""
+mutable struct SGLD{
+    I<:AbstractIntegrator,
+    F<:AbstractFloat
+} <: AbstractTrajectory{I}
+    integrator      :: I
+    n_steps         :: Int  # number of samples
+    ϵ               :: F    # constant scale factor of the learning rate
+    i               :: Int  # iteration counter
+    γ               :: F    # scaling constant
+end
+
+function transition(
+    rng::AbstractRNG,
+    τ::SGLD,
+    h::Hamiltonian,
+    z::PhasePoint
+) where {T<:Real}
+    # z′ = step(rng, τ.integrator, h, z, τ.n_steps)
+    DEBUG && @debug "compute current step size..."
+    # γ = .35
+    τ.i += 1
+    ϵ_t = τ.ϵ / τ.i ^ τ.γ # NOTE: Choose γ=.55 in paper
+
+    DEBUG && @debug "recording old variables..."
+    θ = z.θ
+    # ToDo: how to get stochastic gradient
+    grad = -z.ℓπ.gradient
+
+    DEBUG && @debug "update latent variables..."
+    θ .+= ϵ_t .* grad ./ 2 .+ rand.(Normal.(zeros(length(θ)), sqrt(ϵ_t)))
+
+    # no M-H step
+    z = PhasePoint(h, θ, -z.r)
+    stat = (
+        step_size=τ.integrator.ϵ,
+        n_steps=τ.n_steps,
+        log_density=z.ℓπ.value,
+        hamiltonian_energy=energy(z),
+        )
+    return Transition(z, stat)
+end