Approximate Bayesian computation methods
نویسندگان
چکیده
منابع مشابه
Approximate Bayesian Computation
Just when you thought it was safe to go back into the water, I’m going to complicate things even further. The Nielsen-Wakely-Hey [5, 3, 4] approach is very flexible and very powerful, but even it doesn’t cover all possible scenarios. It allows for non-equilibrium scenarios in which the populations from which we sampled diverged from one another at different times, but suppose that we think our ...
متن کاملAdaptive approximate Bayesian computation
Sequential techniques can enhance the efficiency of the approximate Bayesian computation algorithm, as in Sisson et al.’s (2007) partial rejection control version. While this method is based upon the theoretical works of Del Moral et al. (2006), the application to approximate Bayesian computation results in a bias in the approximation to the posterior. An alternative version based on genuine im...
متن کاملApproximate Bayesian Computation
Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices amon...
متن کاملA comparison of emulation methods for Approximate Bayesian Computation
Approximate Bayesian Computation (ABC) is a family of statistical inference techniques, which is increasingly used in biology and other scientific fields. Its main benefit is to be applicable to models for which the computation of the model likelihood is intractable. The basic idea of ABC is to empirically approximate the model likelihood by using intensive realizations of model runs. Due to co...
متن کاملApproximate Bayesian Computation and MCMC
For many complex probability models, computation of likelihoods is either impossible or very time consuming. In this article, we discuss methods for simulating observations from posterior distributions without the use of likelihoods. A rejection approach is illustrated using an example concerning inference in the fossil record. A novel Markov chain Monte Carlo approach is also described, and il...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Statistics and Computing
سال: 2012
ISSN: 0960-3174,1573-1375
DOI: 10.1007/s11222-012-9350-8