By definition, D n ( x , t ) = 1 n ! lim τ τ → → 0 1 τ τ μ μ n ( t | x , t − − τ τ ) This definition works because μ μ n ( t | x , t ) = 0 , as those are the central moments of the Dirac delta function. Since the even central moments are nonnegative, we have D 2 n ≥ ≥ 0 for all n ≥ ≥ 1 . When the stochastic process is the Markov process d X = b d t + σ σ d W t , we can directly solve for p ( x , t | x , t − − τ τ ) as approximated by a normal distribution with mean x + b ( x ) τ τ and variance σ σ 2 τ τ . This then allows us to compute the central moments, and so D 1 = b , D 2 = 1 2 σ σ 2 , D 3 = D 4 = ⋯ ⋯ = 0 This then gives us the 1-dimensional Fokker–Planck equation: ∂ ∂ t p = − − ∂ ∂ x ( b p ) + 1 2 ∂ ∂ x 2 ( σ σ 2 p )