Bayesian inference for partially observed stochastic differential equations driven by fractional Brownian motion
分数布朗运动驱动的部分观测随机微分方程的贝叶斯推断
We consider continuous-time diffusion models driven by fractional Brownian motion. Observations are assumed to possess a nontrivial likelihood given the latent path. Due to the non-Markovian and high-dimensional nature of the latent path, estimating posterior expectations is computationally challenging. We present a reparameterization framework based on the Davies and Harte method for sampling stationary Gaussian processes and use it to construct a Markov chain Monte Carlo…