In this chapter we provide an overview of data modeling and describe the formulation of probabilistic models. We introduce random variables, their probability distributions, associated probability densities, examples of common densities, and the fundamental theorem of simulation to draw samples from discrete or continuous probability distributions. We then present the mathematical machinery required in describing and handling probabilistic models, including models with complex variable dependencies. In doing so, we introduce the concepts of joint, conditional, and marginal probability distributions, marginalization, and ancestral sampling.

As probability distributions form the cornerstone of statistics, a survey is made of the common families of distributions, including the binomial distribution, Poisson distribution, multinomial distribution, Gaussian distribution, gamma distribution, beta distribution, von Mises distribution, extreme value distributions, t-distribution and chi-squared distribution. Other topics include maximum likelihood estimation, Gaussian mixtures and kernel density estimation.

This chapter defines the COM–Poisson distribution in greater detail, discussing its associated attributes and computing tools available for analysis. This chapter first details how the COM–Poisson distribution was derived, and then describes the probability distribution, and introduces computing functions available in R that can be used to determine various probabilistic quantities of interest, including the normalizing constant, probability and cumulative distribution functions, random number generation, mean, and variance. The chapter then outlines the distributional and statistical properties associated with this model, and discusses parameter estimation and statistical inference associated with the COM–Poisson model. Various processes for generating random data are then discussed, along with associated available R computing tools. Continued discussion provides reparametrizations of the density function that serve as alternative forms for statistical analyses and model development, considers the COM–Poisson as a weighted Poisson distribution, and details discussion describing the various ways to approximate the COM–Poisson normalizing function.

Chapter 6 explains the CUDA random number generators provided by the cuRAND library. The CUDA XORWOW generator was found to be the fastest generator in the cuRAND library. The classic calculation of pi by generating random numbers inside a square is used as a test case for the various possibilities on both host CPU and the GPU. A kernel using separate generators for each thread is able to generate about1012 random numbers per second and is about 20 000 times faster than the simplest host CPU version running on a single core. The inverse transform method for generating random numbers from any distribution is explained. A 3D Ising model calculation is presented as a more interesting application of random numbers.The Ising example has a simple interactive GUI based on OpenCV.

Chapter 6 explains the CUDA random number generators provided by the cuRAND library. The CUDA XORWOW generator was found to be the fastest generator in the cuRAND library. The classic calculation of pi by generating random numbers inside a square is used as a test case for the various possibilities on both host CPU and the GPU. A kernel using separate generators for each thread is able to generate about1012 random numbers per second and is about 20 000 times faster than the simplest host CPU version running on a single core. The inverse transform method for generating random numbers from any distribution is explained. A 3D Ising model calculation is presented as a more interesting application of random numbers.The Ising example has a simple interactive GUI based on OpenCV.

This chapter provides techniques for analyzing discrete-time dynamical systems under probabilistic input uncertainty. Here, the relation between the input and the state is described by a discrete-time state-space model. The input vector is modeled as a vector-valued stochastic process with known first and second moments (or known pdf). The first part of the chapter is devoted to the analysis of linear systems and provides techniques for characterizing the first and second moments and the pdf of the state vector. The second part deals with the analysis of nonlinear systems, where we use the techniques developed in Chapter 4 to exactly characterize the distribution of the state vector when the pdf of the input vector is given. In addition, we rely on linearization techniques to obtain expressions that approximately characterize the first and second moments and the pdf of the state vector. The third part of the chapter illustrates the application of the techniques developed to the analysis of inertia-less AC microgrids under random active power injections.

Chapter 2 provides a review of probability theory, focusing on the topics that are essential for the remainder of the book. Included are the elements of set theory, the axioms and basic rules of probability theory, the concept of a random variable, discrete and continuous random variables, univariate and multivariate probability distributions, reliability and hazard functions, expectation and statistical moments, distributions and moments of functions of random variables, and extreme-value distributions. Appendix A presents commonly used probability distribution models with their properties, for easy reference. Thorough mastery of the material in this chapter is essential for understanding the remainder of the book.

The statistical analysis of geochemical data employs the main statistical techniques of averaging, probability distributions, correlation, regression, multivariate analysis and discriminant analysis. A particular problem with major element geochemical data is that it is constrained; that is, the compositions sum to 100% and the data are ‘closed’. A related problem arises when ternary plots are used to display geochemical data. Techniques are described to accommodate the problems associated with compositional data which include log-ratio conversions and the biplot diagram. Further statistical problems arise in the area of ratio correlation as advocated in Pearce element ratio diagrams, which is not recommended. Applications to trace elements and radiogenic isotope correlations are discussed. The details of discriminant analysis are outlined as a prelude to a more detailed discussion of tectonic discrimination diagrams considered in Chapter 5.

We introduce finite probability distributions and their use as a model of an ecological community. We define Shannon entropy, give examples, and establish its basic properties. We interpret Shannon entropy in terms of both coding and diversity, and prove that it is uniquely characterized by the chain rule.

In this chapter, there are two types of probabilities that can be estimated: empirical probability and theoretical probability. Empirical probability is calculated by conducting a number of trials and finding the proportion that resulted in each outcome. Theoretical probability is calculated by dividing the number of methods of obtaining an outcome by the total number of possible outcomes. Adding together the probabilities of two different events will produce the probability that either one will occur. Multiplying the probabilities of two events together will produce the probability that both will occur at the same time or in succession. As the number of trials increases, the empirical probability and theoretical probability converge.

It is possible to build a histogram of empirical or theoretical probabilities. As the number of trials increases, the empirical and theoretical probability distributions converge. If an outcome is produced by adding together (or averaging) the results of events, the probability distribution is normally distributed. Because of this, it is possible to make inferences about the population based on sample data – a process called generalization. The mean of sample means converges to the population mean, and the standard deviation of means (the standard error) converges on the value.

This chapter relaxes the assumption of determistic population growth. It starts with a motivation for the inclusion of stochastic elements in population models and introduces two types of stochasticity: environmental stochasticity and demographic stochasticity. After this, three main probability distributions are introduced: the normal, the binomial and the Poisson. Using these distributions, the dynamics of a small population are simulated and the simulation outcome analysed statistically to assess the viability of the population. A fourth section provides some mathematical background of stochastic population models and population viability analysis. The chapter concludes with an outline of the International Union for Conservation of Nature's risk model to assess the endangerment of species.