Bayesian statistics defines how new information, given by a likelihood, should be combined with previously acquired information, given by a prior distribution. Many experiments have shown that humans make use of such priors in cognitive, perceptual, and motor tasks, but where do priors come from? As people never experience the same situation twice, they can only construct priors by generalizing...

To make fast and accurate behavioral choices, we need to integrate noisy sensory input, take prior knowledge into account, and adjust our decision criteria. It was shown previously that in two-alternative-forced-choice tasks, optimal decision making can be formalized in the framework of a sequential probability ratio test and is then equivalent to a diffusion model. However, this analogy hides ...

This paper proposes a new algorithm for Bayesian model determination in Gaussian graphical models under G-Wishart prior distributions. We first review recent development in sampling from G-Wishart distributions for given graphs, with a particular interest in the efficiency of the block Gibbs samplers and other competing methods. We generalize the maximum clique block Gibbs samplers to a class o...

where α is an unknown intercept parameter, 1n is an n× 1 vector each component of which is one, XF = (x1, . . . ,xp) is an n×p design matrix, βF is a p×1 vector of unknown regression coefficients, In is an n × n identity matrix and σ 2 is an unknown positive scalar. (The subscript F denotes the full model.) We assume that the columns of XF have been standardized so that for 1 ≤ i ≤ p, x′i1n = 0...

conclusions it can be concluded that the rsm is an effective method for the optimization of recombinant protein expression using synthetic genes in e. coli. results the proposed optimum fermentation condition consisted of 7.81 g l-1 glucose, od 600 nm prior induction 1.66 and induction temperature of 30.27°c. the model prediction of 0.267 g l-1 of rhifn-β and 0.961 g l-1 of acetate at the optim...

This paper proposes an improved dark channel prior for removing haze from images. Dark channel prior is an effective method for removing haze. Dark channel is an image in the same size as the hazy image which is obtained by dividing the RGB images into windows and for each window, the minimum of each R, G and B channels are calculated. Then again the minimum of these three values is calculated ...