Approximate Bayesian computation is a really cool technique for fitting basically any stochastic model, intended for models where the likelihood is intractable (say, you can sample from the model if you fix the parameters but you cannot

numerically, algorithmically or analyticallycalculate the likelihood). When introducing approximate Bayesian computation (ABC) to an audience it is nice to use some example model that is really simple but still somewhat interestingandthat has an intractable likelihood.

What would be a good example of a really simple model that still has an intractable likelihood?

**Answer**

Two distributions that are used a lot in the literature are:

- The g-and-k distribution. This is defined by its quantile function (inverse cdf) but has an intractable density. Rayner and MacGillivray (2002) is a good overview of these, and one of many ABC papers which use it as a toy example is Drovandi and Pettitt (2011).
- Alpha stable distributions. These are defined by their characteristic function but have an intractable density except for a couple of special cases. This has applications in finance and is often used in sequential ABC papers, for example Yildirim et al (2013).

**Attribution***Source : Link , Question Author : Rasmus Bååth , Answer Author : Dennis Prangle*