This paper takes a Bayesian-decision theoretic approach to transfer function estimation, nominal model estimation, and quantification of the resulting model error. Consistency of the nonparametric estimate of the transfer function is proved together with a rate of convergence. The required quantities can be computed routinely using reversible jump Markov chain Monte Carlo methods. The proposed ...

Even for simple perceptual decisions, the mechanisms that the brain employs are still under debate. Although current consensus states that the brain accumulates evidence extracted from noisy sensory information, open questions remain about how this simple model relates to other perceptual phenomena such as flexibility in decisions, decision-dependent modulation of sensory gain, or confidence ab...

Learning the enormous number of parameters is a challenging problem in model-based Bayesian reinforcement learning. In order to solve the problem, we propose a model-based factored Bayesian reinforcement learning (F-BRL) approach. F-BRL exploits a factored representation to describe states to reduce the number of parameters. Representing the conditional independence relationships between state ...

Many phenomena in animal learning can be explained by a context-learning process whereby an animal learns about different patterns of relationship between environmental variables. Differentiating between such environmental regimes or 'contexts' allows an animal to rapidly adapt its behaviour when context changes occur. The current work views animals as making sequential inferences about current...

One of the main limitations in predictive analytics is the acquisition cost of engineering data due to slow-running computer code or expensive experiments. Also, data is often multi-dimensional and highly non-linear in nature, causing problems for standard statistical predictive models. Once data is collected and models are built, many applications require accurate and scalable uncertainty quan...

How should we evaluate the effect of a policy on likelihood an undesirable event, such as conflict? The significance test has three limitations. First, relying statistical misses fact that uncertainty is continuous scale. Second, focusing standard point estimate overlooks variation in plausible sizes. Third, criterion substantive rarely explained or justified. A new Bayesian decision-theoretic ...

It is somewhat true that in most mainstream statistical literature the transition from inference to a formal decision model is seldom explicitly considered. Since the Markov chain Monte Carlo (MCMC) revolution in Bayesian statistics, focus has generally been on the development of novel algorithmic methods to enable comprehensive inference in a variety of applications, or to tackle realistic pro...

A previous paper (2] showed how to generate a linear discriminant network (LDN) that computes likely faults for a noisy fault detection problem by using a modification of the perceptron learning algorithm called the pocket algorithm. Here we compare the performance of this connectionist model with performance of the optimal Bayesian decision rule for the example that was previously described. W...