U.S. COMMODITY FUTURES TRADING COMMISSION Division of Market Oversight

Abstract — Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been

We show how to use “complementary priors ” to eliminate the explaining away effects that make inference difficult in densely-connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer

of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands

In this report, we compare and contrast three previously published Bayesian methods for inferring haplotypes from genotype data in a population sample. We review the methods, emphasizing the differences between them in terms of both the models ("priors") they use and the computational

that the FM model is a degenerate version of the HMRF model. The advantage of the HMRF model derives from the way in which the spatial information is encoded through the mutual influences of neighboring sites. Although MRF modeling has been employed in MR image segmentation by other researchers, most reported

;belief revision") version, Weiss For the case of networks with multiple loops, Richard son To summarize, what is currently known about loopy propagation is that ( 1) it works very well in an error correcting code setting and (2) there are conditions for a single-loop network for which it can be guaranteed

... In this paper we present two general types of Gibbs samplers that can be used to fit posteriors of Bayesian hierarchical models based on stick-breaking priors. The first type of Gibbs sampler, referred to as a Polya urn Gibbs sampler, is a generalized version of a widely used Gibbs sampling

strengthen its alignment with curriculum and instructional practices in high school and college. An important assumption underlying the changes to the SAT ® was that scores on

Hampshire, Purdue, RAND and Washington University. The views expressed in this paper are not necessarily those of any institution with which I am affiliated, and I am solely responsible for any errors. The views expressed herein are those of the authors and not necessarily those of the National Bureau of Economic Research.