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Lord G: An Introduction to Stochastic
Modeling

An Introduction to Stochastic Modeling

A Concise Handbook of Essential Techniques

Basic Statistics for Life Scientists

Pelican Books

'Probably the UK's greatest living statistician' Telegraph From the UK's 'statistical national treasure', a clever and data-driven guide to how we can live with risk and uncertainty We live in a world where uncertainty is inevitable. How should we deal with what we don't know? And what role do chance, luck and coincidence play in our lives? David Spiegelhalter has spent his career dissecting data in order to understand risks and assess the chances of what might happen in the future. In The Art of Uncertainty, he gives readers a window onto how we can all do this better. In engaging, crystal-clear prose, he takes us through the principles of probability, showing how it can help us think more analytically about everything from medical advice to pandemics and climate change forecasts, and explores how we can update our beliefs about the future in the face of constantly changing experience. Along the way, he explains why roughly 40% of football results come down to luck rather than talent, how the National Risk Register assesses near-term risks to the United Kingdom, and why we can be so confident that two properly shuffled packs of cards have never, ever been in the exact same order. Drawing on a wide range of captivating real-world examples, this is an essential guide to navigating uncertainty while also having the humility to admit what we do not know

How to Navigate Chance, Ignorance, Risk and Luck

The Art of Uncertainty

Theory, Methods, and Computation

Elements of Stochastic Finance

Chapman & Hall/CRC Biostatistics Series

Age-Period-Cohort Models

This book introduces factor analysis methods for the study of multivariate stochastic processes whose structure evolves over time. It introduces the theory of factor analysis and evolutionary stochastic processes and combines that to study evolutionary factor models. The book includes many real examples to illustrate applications from such fields as macroeconomics, finance, neuroscience, communication, and psychology. MATLAB® and R code is included throughout the book for implementation of the methods.

Evolutionary Factor Analysis

Scaling (zooming in or out) is taking a close or distant view of an object. Infinite scaling (the scaling parameter tends to zero or infinity) may lead to a limit which is self-similar and much simpler than the original object. For instance, the classical Donsker's theorem says that all weakly dependent stationary processes with finite variance scale to Brownian motion at large scales. In the case of a random field indexed by two-dimensional parameter, both types of scaling can be anisotropic, meaning that the horizontal and vertical axes are scaled at different rate determined by the ratio γ&gt;0 of the scaling exponents along the axes. The natural questions under such scaling are whether the scaling limits exist for any γ&gt;0, and what are these limits are. The book tries to answer these questions. It introduces the concept of scaling transition and discusses its existence for a natural class of planar random fields including Gaussian, linear and some nonlinear ones. The scaling limits are identified and exhibit a surprising trichitomy at a critical point γ0, with 'unbalanced' limits having unusual path properties and dependence structure along one of the coordinate axes. Scaling transition occurs in applied sciences (telecommunications and econometrics) when aggregating independent processes with long-range dependence in which case 'unbalanced' limits are classical Gaussian or stable random fields.

Anisotropic Scaling Of Random Fields With Long-range Dependence: Scaling Limits Of Random Fields With Applications

Demonstrates how to calculate these metrics to compare the health status of different countries and explores the world of infectious diseases. Tests various machine learning tools for analyzing trends and relationships among key variables, aiming to prevent unexpected outcomes.

Machine Learning Applications and Spatial Modelling Analysis with R

Health Metrics and the Spread of Infectious Diseases

Handbook of Statistics

Markov Chains: Theory and Applications: Volume 52

A Statistical Guide to Certainty in an Uncertain World

What Are the Odds?

Oxford Statistical Science Series

This new, updated second edition of In All Likelihood explores the central role of likelihood in a wide spectrum of statistical problems, ranging from simple comparisons-such as evaluating accident rates between two groups-to sophisticated analyses involving generalized linear models and semiparametric methods. Rather than treating likelihood merely as a tool for point estimation, the book highlights its broader value as a foundational framework for constructing, understanding and computational implementation of statistical models. It emphasizes how likelihood perspectives inform model development, assessment, and inference in a cohesive and intuitive way. While grounded in essential mathematical theory, the book adopts an informal and accessible approach, using heuristic reasoning and illustrative, realistic examples to convey key ideas. It avoids overly contrived problems that yield to theoretically clean and closed-form solutions, instead embracing more realistic and complex real-world data analysis made tractable by modern computing resources. This perspective helps focus attention on the statistical reasoning behind model choice and interpretation. The text also integrates a wide range of modern topics that extend classical likelihood theory, including generalized and hierarchical generalized linear models, nonparametric smoothing techniques, robust methods, the EM algorithm, and empirical likelihood. Suitable for students, researchers, and practitioners, this book provides both foundational insights and contemporary perspectives on likelihood-based statistical modelling.

Statistical Modelling and Inference Using Likelihood

In All Likelihood

This unique compendium develops a general approach to building models of economic and financial processes, with a focus on statistical learning techniques that scale to large data sets. It introduces the key elements of a parametric statistical model: likelihood, prior, and posterior, and show how to use them to make predictions.The book covers classical techniques such as multiple regression and the Kalman filter in a clear, accessible style that has been popular with students, but also includes detailed treatments of state-of-the-art models, highlighting tree-based methods, support vector machines and kernel methods, deep learning, and reinforcement learning. Theories are supplemented by real-world examples.This reference text is useful for undergraduate, graduate and even PhD students in quantitative finance, and also to practitioners who are facing the reality that data science and machine learning are disrupting the industry.

Statistical Machine Learning With Applications In Finance

Cambridge Studies in Advanced Mathematics

Gaussian Free Field and Liouville Quantum Gravity

An Introduction to Stochastic Modeling

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