Towards Machine Wald

The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed \emph{by humans} because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to \emph{think} as \emph{humans} have the ability to do when faced with uncertainty is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tend to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with Decision Theory, Machine Learning, Bayesian Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty Quantification and Information Based Complexity.Comment: 37 page

Proteomic Approaches for Studying the Phases of Wound Healing

© 2009, Springer Berlin Heidelberg. Proteome level information is necessary to understand the function of specific cell types and their roles in health and disease. Proteomics is a rapidly developing field with a wide range of applications in wound healing. The ability to use proteomics to assess the wound healing process would have many benefits, including earlier evidence of healing and better understanding of how different treatments affect the wound at the protein level. The basis of what is known about the chronic wound proteome is based on results from a broad collection of studies utilizing a number of different proteomic techniques on fluids and tissues from wounds with different etiologies. The identification of biomarkers associated with healing or delayed healing in chronic wounds could have great significance in the use of current treatments, as well as in the development of new therapeutic interventions