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Introduction to Bayesian

Despite being named after a reverend who died over 250 years ago, the term ‘Bayesian’ has recently seen a surge in interest in modelling and analytics circles. One of the reasons for this is the huge popularity of Nate Silver’s book ‘The Signal and the Noise’ which describes in layman’s terms the theory behind his astonishingly accurate US election predictions. Whilst Bayesian theory is only one of several types of mathematics used by Nate Silver, it reflects a growing awareness that modern technology can be used to harness the power of techniques that were previously considered too hard to put into practice.

‘Bayesian’ is of particular interest to marketing analysts in that it enables us to deal with the very real degree of uncertainty that we face in designing models for practical application in the real world. In also enables us to draw conclusions for smaller amounts of data or when the data is hard to come by as well as incorporating prior knowledge into models. The main advantage of ‘Bayesian’ over traditional (or ‘Frequentist’ approaches, as they are known) is more accurate predictions.

A lot of the content written about ‘Bayesian’ appears aimed at the academic or mathematician. We’ve therefore compiled our own introduction which can be downloaded from the link below.

Inside every non Bayesian there’s a Bayesian struggling to get out
Dennis V. Lindley
Decision Theorist

Download our free white paper

An introduction to Bayesian for Marketers