• Approaches the computational/algorithmic point of view in greater detail
• Integral representations of conditional expectations as operators in vector spaces and illustrated it for special cases
• Existence of projective limits of regular conditional measures
• Novel application of Weil-Mackey-Bruhat formula
• Dichotomy of absolute continuity and singularity of probability measures
• Conditioning in a W*-algebra context and a related Kolmogorov existence theorem
• "Free independence," Gleason measure, and quantum probability
• Martingale convergence and sufficiency in the W*-algebra context
• And much more

In response to unanswered difficulties in the generalized case of conditional expectation and to treat the topic in a well-deservedly thorough manner, M.M. Rao gave us the highly successful first edition of Conditional Measures and Applications. Until this groundbreaking work, conditional probability was relegated to scattered journal articles and mere chapters in larger works on probability. This second edition continues to offer a thorough treatment of conditioning while adding substantial new information on developments and applications that have emerged over the past decade.

Conditional Measures and Applications, Second Edition clearly elucidates the subject, from fundamental principles to abstract analysis. The author illustrates the computational difficulties in evaluating conditional probabilities in nondiscrete cases with numerous examples, demonstrates applications to Markov processes, martingales, potential theory, and Reynolds operators as well as sufficiency in statistics, and clarifies ideas in modern noncommutative probability structures through conditioning in general structures, including parts of operator algebras and "free" random variables. He also discusses existence and construction problems from the Bishop-Brouwer constructive analysis point of view.

With open problems in every chapter and links to other areas of mathematics, this invaluable second edition offers complete coverage of conditional probability and expectation and their structural analysis, from simple to advanced abstract levels, for both novices and seasoned mathematicians.