We consider the problem of inferring fold changes in gene expression from cDNA microarray data. Standard procedures focus on the ratio of measured fluorescent intensities at each spot on the microarray, but to do so is to ignore the fact that the variation of such ratios is not constant. Estimates of gene expression changes are derived within a simple hierarchical model that accounts for measurement error and fluctuations in absolute gene expression levels. Significant gene expression changes are identified by deriving the posterior odds of change within a similar model. The methods are tested via simulation and are applied to a panel of Escherichia coli microarrays.

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This paper aims to explore mechanistic and teleological explanations of consciousness. In terms of mechanistic explanations, it critiques various existing views, especially those embodied by existing computational cognitive models. In this regard, the paper argues in favor of the explanation based on the distinction between localist (symbolic) representation and distributed representation (as formulated in the connectionist literature), which reduces the phenomenological dierence to a mechanistic dierence. Furthermore, to establish a teleological explanation of consciousness, the paper discusses the issue of the functional role of consciousness on the basis of the afore-mentioned mechanistic explanation. A proposal based on synergistic interaction between the conscious and the unconscious is advanced that encompasses various existing views concerning the functional roles of consciousness. This two-step deepening explanation has some empirical support, in the form of a cognitive model...

It is hypothesized that 680 million years ago solar magnetic storms producing ultraviolet and X-radiation affected the earths ozone layer, which in turn influenced the variations in the silt deposition from glacial run-off. Preserved as fossils discovered in South Australia, the striation widths constitute clues to ancient solar activity. Utilizing this noisy data, we have improved our ability to predict the modern sunspot series. In this paper, we detail how the prediction results were achieved through training on the fossil data and committee predictions with the sunspots. Through this exercise, we develop general methods for combining predictors and also time series that may be related but separated in time. 1. Introduction: Fossils and Sunspots One of the most studied time series corresponds to the record of sunspots dating back to the 1700's. New insight 1700 1994 Precambrian Period Modern Man Fossil Data Sunspots Figure 1. Fossil data and sunspot numbers. into this series com...

This paper presents a handful of Bayesian tools one can use to model heterogeneous event counts.

This paper aims at evaluating existing computational (mechanistic) models of cognition in relation to the study of consciousness, on the basis of psychological and philosophical theories and data. It rst critiques various mechanistic explanations of consciousness, especially existing computational cognitive models. It then explores the issue of the functional roles of consciousness and examines various views in this regard, in relation to the mechanistic explanation of consciousness. In these examinations, the paper argues in favor of the explanation based on the distinction between localist (symbolic) representation and distributed representation (the ideas of which have been put forth in the connectionist literature). Serving as a basis for the discussions, a model of the conscious/unconscious interaction, utilizing the representational dierence explanation of consciousness, is briey described. The paper also advances a proposal regarding the synergistic interaction between the co...

We consider the problem of inferring fold changes in gene expression from cDNA microarray data. Standard procedures focus on the ratio of measured # uorescent intensities at each spot on the microarray, but to do so is to ignore the fact that the variation of such ratios is not constant. Estimates of gene expression changes are derived within a simple hierarchical model that accounts for measurement error and # uctuations in absolute gene expression levels. Signi# cant gene expression changes are identi# ed by deriving the posterior odds of change within a similar model. The methods are tested via simulation and are applied to a panel of Escherichia coli microarrays.

This note considers estimation of the mean of a multivariate Gaussian distribution with known variance within the Minimum Message Length (MML) framework. Interestingly, the resulting MML estimator exactly coincides with the positive-part James-Stein estimator under the choice of an uninformative prior. A new approach for estimating parameters and hyperparameters in general hierarchical Bayes models is also presented.

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Ridge regression is re-examined and ridge estimators based on prior information are introduced. A necessary and sufficient condition is given for such ridge estimators to yield estimators of every non-null linear combination of the regression coefficients with smaller,meaH square error than that of the Gauss-Markov best linear unbiased estimator.