  There is an increasing amount of literature focused on Bayesian computational
methods to address problems with intractable likelihood. One approach is a set
of algorithms known as Approximate Bayesian Computational (ABC) methods. One of
the problems of these algorithms is that the performance depends on the tuning
of some parameters, such as the summary statistics, distance and tolerance
level. To bypass this problem, Mengersen, Pudlo and Robert (2013) introduced an
alternative method based on empirical likelihood, which can be easily
implemented when a set of constraints, related to the moments of the
distribution, is known. However, the choice of the constraints is sometimes
challenging. To overcome this problem, we propose an alternative method based
on a bootstrap likelihood approach. The method is easy to implement and in some
cases it is faster than the other approaches. The performance of the algorithm
is illustrated with examples in Population Genetics, Time Series and Stochastic
Differential Equations. Finally, we test the method on a real dataset.
