last updated: October 24, 2024

Bayesian Statistics

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4.6 The limitations of conjugate pairs

The main advantage of conjugate priors is that, when we can use them, Bayesian updates are simple to perform. Their main disadvantage is that, in many cases, we cannot use them. This can occur in two main ways:

  • Our chosen model family does not have any conjugate priors.

  • Our chosen model family does have conjugate priors, but there are no choices of prior parameters that result in a conjugate prior that matches our prior beliefs.

    This case is particularly likely to happen if we use a prior based on expert opinions or on earlier experimental work.

For that reason the modern approach to Bayesian learning largely relies on the computational techniques introduced in Chapter 8.

Exercise 6.6 shows that mixtures of conjugate priors can be handled with similar ease to conjugate priors. This can help if we need to manufacture a prior distribution to reflect particular properties, but it only provides enough help in a small fraction of situations.