Bayesian Methods for Structural Dynamics introduces recently developed Bayesian methods and applications to several areas of engineering. Readers are provided a through grounding in the theory, and shown concrete examples to promote easier understanding. The first two chapters give a general introduction and literature review of the applications of Bayesian methods in different disciplines of engineering, while giving simple examples of static systems to illustrate the concepts. Yuen goes on to introduce time-domain approaches for unmeasured input, which can be applied to multi-degree-of-freedom linear systems subjected to stationary or non-stationary input, as demonstrated with earthquake ground motion. The author presents the Bayesian spectral density approach in the fourth chapter, using hydraulic jump to demonstrate the methodology and providing comparisons of applicability between time-domain and frequency-domain approaches. In chapter five, Yuen addresses the problem of model parameter identification through eigenvalue-eigenvector measurements, with applications to finite-element model updating and structural health monitoring. Chapter 6 considers the problem of selection of model class for system identification, and introduces Markov Chain Monte Carlo simulation and Metropolis-Hastings algorithm. Model class selection is then illustrated by problems in air-quality prediction, artificial neural networks, and seismic attenuation.