The jump-diffusion framework encompasses most affine and nonaffine one-factor models used in finance. Due to the model complexity of this framework, particle filters and combinations of Gibbs and Metropolis-Hastings samplers have been the tools of choice for its estimation. However, recent research has shown that the discrete nonlinear filter (DNF) can also be used for fast and accurate maximum likelihood estimation of jump-diffusion models. We present a combination of the DNF with Markov chain Monte Carlo (MCMC) methods for Bayesian estimation in the spirit of the particle MCMC algorithm. In addition, we show that option prices can be easily included into the DNF’s likelihood evaluations even in the nonaffine case, which allows for efficient joint Bayesian estimation. We finally present joint estimation results using affine and nonaffine models and S&P 500 data.