Bayesian methods are a powerful tool in many areas of science and engineering, especially statistical physics, medical sciences, electrical engineering, and information sciences. They are also ideal for civil engineering applications, given the numerous types of modeling and parametric uncertainty in civil engineering problems. For example, earthquake ground motion cannot be predetermined at the structural design stage. Complete wind pressure profiles are difficult to measure under operating conditions. Material properties can be difficult to determine to a very precise level - especially concrete, rock, and soil. For air quality prediction, it is difficult to measure the hourly/daily pollutants generated by cars and factories within the area of concern. It is also difficult to obtain the updated air quality information of the surrounding cities. Furthermore, the meteorological conditions of the day for prediction are also uncertain. These are just some of the civil engineering examples to which Bayesian probabilistic methods are applicable. Familiarizes readers with the latest developments in the field + Includes identification problems for both dynamic and static systems + Addresses challenging civil engineering problems such as modal/model updating + Presents methods applicable to mechanical and aerospace engineering + Gives engineers and engineering students a concrete sense of implementation + Covers real-world case studies in civil engineering and beyond, such as: structural health monitoring + seismic attenuation + finite-element model updating + hydraulic jump + artificial neural network + air quality prediction + Includes other insightful daily-life examples + Companion website with MATLAB code downloads for independent practice.