What does MCMCM stand for?

He introduces Bayesian inference and modeling, then centers on computation, including Markov chain Monte Carlo methods and Metropolis or Metropolis-Hastings algorithms, perfect sampling, posterior and likelihood approximations, residuals, and goodness-of-fit.

The original material--covering Markov chain Monte Carlo methods, derivative pricing using jump diffusion with closed-form formulas, value at risk calculation using extreme value theory base on a nonhomogeneous two-dimensional Poisson process, and multivariate volatility models with time-varying correlations--has been expanded to include discussion consistent covariance estimation under heteroscedasticity and serial correlation, alterative approaches to volatility modeling, financial factor models, stat-space models, Kalman filtering, and estimation of stochastic diffusion models.

Markov Chain Monte Carlo methods can readily be used, see Ref.