Working In Uncertainty

Mathematics

Getting more value from mathematics

• Mathematics as modelling toolkits: philosophical and practical aspects (2021, groundbreaking theoretical and practical proposals)
• Efficient mathematics (2017, proposals)
• Automating open-mindedness (2014, article)
• Learning and teaching probability and statistics (2014, article)
• A way of thinking about probability (2005, article)
• Practical probability (2003, article)

Writing mathematics more clearly

• Specifying regression (2019, detailed proposals)
• Eliminating imaginary numbers (2017, detailed guide)
• Eliminating infinity (2016, detailed guide)
• Taming probability notation (2014, article)
• How to write mathematics clearly (2009, detailed guide)
• The Campaign for Plain Maths starts here (2005, article)

Bayesian ideas and techniques

These articles, with code, explain simple Bayesian ideas and techniques. The ‘recipes’ have Excel code.

• Probability axioms in Bayesian language (2014, detailed proposals)
• Relative probabilities (2014, 2019, 2022, detailed proposals)
• Bayesian recipes (1): Simple, efficient statistics for rates (2013, detailed methods)
• Bayesian recipes (2): Simple, efficient statistics for rare(ish) events (2013, detailed methods)
• Bayesian recipes (3): Simple, efficient statistics for Normal distributions (2013, detailed methods)

Published book

A pocket guide to risk mathematics, a book by Matthew Leitch

Although this book is aimed specifically at auditors, its clear explanations and tips on faults to watch out for are useful for anyone with an interest in the topic. What emerges is that ‘risk’ in mathematics is a young and poorly formulated idea when compared with the long heritage of 'probability' and 'value'. The book explains several common mistakes in 'risk analysis'.

These really are key ideas that every auditor should know.

Made in England