People can apparently make surprisingly sophisticated inductive inferences, despite the fact that there are constraints on cognitive resources that would make performing exact Bayesian inference computationally intractable. What algorithms could they be using to make this possible? We show that a simple sequential algorithm, Win-Stay, Lose-Shift (WSLS), can be used to approximate Bayesian infer...

In this article, a special type of orthogonalization is obtained to construct a multiple input transfer function model. By using this technique, construction of a transfer function model is divided to sequential construction of transfer function models with less input time series. Furthermore, based on real and simulated time series we provide an instruction to adequately perform the stages of ...

Sequential techniques can be added to the approximate Bayesian computation (ABC) algorithm to enhance its efficiency. Sisson et al. (2007) introduced the partial rejection control version of this algorithm to improve upon existing Markov chain versions of the algorithm. While Sisson et al.’s (2007) method is based upon the theoretical developments of Del Moral et al. (2006), the application to ...