robability and tatistics the purpose of this is to establish the baseline probability and statistics background for this book we review basic concepts such as the sum and product rules of probabilityrandom variables and their probability distributionsexpectationsindependenceconditional probabilitytransformation ruleslimit theoremsand markov chains the properties of the multivariate normal distribution are discussed in more detail the main ideas from statistics are also reviewedincluding estimation techniques (such as maximum likelihood estimation)confidence intervalsand hypothesis testing random experiments and probability spaces the basic notion in probability theory is that of random experimentan experiment whose outcome cannot be determined in advance mathematicallya random experiment is modeled via triplet (ohmhp)whereohm is the set of all possible outcomes of the experimentcalled the sample space random experiment sample space is the collection of all subsets of ohm to which probability can be assignedsuch subsets are called events events is probability measurewhich assigns to each event number [abetween and indicating the likelihood that the outcome of the random experiment lies in probability measure any probability measure must satisfy the following kolmogorov axiomskolmogorov axioms [ for every event [ohm for any sequence of eventshi ai [ai ] with strict equality whenever the events are disjoint (that isnon-overlapping (