Dennis Victor Lindley (1923–2013) was one of the most consequential figures in the Bayesian revolution of the twentieth century. Originally trained in the Neyman-Pearson frequentist tradition, he underwent a dramatic intellectual conversion in the 1950s, coming to believe that all of statistical inference should be based on Bayes' theorem, subjective probability, and the axioms of coherent decision-making. For the next five decades, he championed this view with clarity, elegance, and tenacity, becoming the foremost advocate and systematizer of Bayesian statistics in the English-speaking world.
Early Life and Frequentist Training
Lindley was born in London and studied mathematics at Trinity College, Cambridge, during World War II. After the war, he worked with the leading frequentists of the day and made early contributions to confidence interval theory. However, his engagement with the ideas of Jeffreys, Ramsey, de Finetti, and especially Savage led him to question the foundations of the frequentist approach. By the late 1950s, his conversion to Bayesianism was complete.
Lindley's Paradox
One of Lindley's most famous contributions is Lindley's paradox (1957), which demonstrated a striking conflict between Bayesian and frequentist hypothesis testing. He showed that with a sufficiently large sample, a result could simultaneously be statistically significant (leading a frequentist to reject the null hypothesis) and yet have strong Bayesian evidence in favor of the null. This paradox highlighted fundamental differences between the two approaches and became a powerful rhetorical tool for Bayesian advocates.
Consider testing whether a coin is fair. With a very large sample, even a tiny deviation from 50% heads will produce a significant p-value. But a Bayesian analysis with a reasonable prior will find that the data strongly favor the fair coin hypothesis, because the observed proportion is so close to 0.5. This divergence grows worse as sample size increases—a fundamental, not merely technical, disagreement.
The Bayesian Evangelist
Lindley's two-volume Introduction to Probability and Statistics from a Bayesian Viewpoint (1965) was one of the first comprehensive Bayesian textbooks. He went on to write influential papers on almost every major topic in Bayesian statistics, including the treatment of nuisance parameters, hierarchical models, and the foundations of scientific inference. His 2006 book Understanding Uncertainty presented Bayesian ideas for a general audience.
“Inside every non-Bayesian, there is a Bayesian struggling to get out.”— Dennis Lindley
University College London and Beyond
Lindley was head of the Department of Statistics at University College London from 1967 to 1977, where he built one of the world's leading centers of Bayesian research. He supervised and inspired a generation of Bayesian statisticians, including Adrian Smith, who would later play a central role in the MCMC revolution. After retiring from UCL, Lindley continued to write and lecture prolifically.
Philosophical Clarity
What distinguished Lindley from many other Bayesians was the clarity and uncompromising rigor of his philosophical position. He argued that the laws of probability are not merely useful but are the only coherent basis for reasoning under uncertainty, and that any departure from Bayesian methods necessarily involves incoherence. This absolutism earned him both deep admiration and fierce opposition, but it also provided the Bayesian movement with a clear intellectual standard.
Born on 25 July in London.
Graduated from Trinity College, Cambridge.
Published the paper introducing Lindley's paradox.
Published Introduction to Probability and Statistics from a Bayesian Viewpoint (two volumes).
Head of Department of Statistics, University College London.
Published Understanding Uncertainty for a general readership.
Died on 14 December in Minehead, Somerset, aged ninety.