In today’s data-driven world, maths is a weapon wielded by banks, insurance companies, tech firms, and government agencies. These organizations use sophisticated algorithms to calculate odds, make predictions, uncover patterns, manage risk, and optimize actions. And they treat you as another number to crunch along the way.

Robin Hood Maths explains the mathematical methods these companies and agencies use to manipulate and profit off of you. It’s easy to assume these algorithms are too complex to even understand, let alone use for yourself. But maths professor Noah Giansiracusa makes the compelling case that anyone can use these same methods, without any special training or advanced knowledge. He offers simple hacks and streamlined formulas for beating the number crunchers at their own game.

With Professor Giansiracusa as your guide, you’ll learn how to use maths to rescue your credit score and make better investments, take control of your social media, and reclaim agency over the decisions you make every day. In a society designed to take from the poor and give to the rich, maths has the potential to be a powerful democratizing force. Robin Hood Maths gives you the tools you need to think for yourself, act in your own best interest, and thrive.

The study of ecological systems is often impeded by components that escape perfect observation, such as the trajectories of moving animals or the status of plant seed banks. These hidden components can be efficiently handled with statistical modeling by using hidden variables, which are often called latent variables. Notably, the hidden variables framework enables us to model an underlying interaction structure between variables (including random effects in regression models) and perform data clustering, which are useful tools in the analysis of ecological data. This book provides an introduction to hidden variables in ecology, through recent works on statistical modeling as well as on estimation in models with latent variables. All models are illustrated with ecological examples involving different types of latent variables at different scales of organization, from individuals to ecosystems. Readers have access to the data and R codes to facilitate understanding of the model and to adapt inference tools to their own data.