for the quality of the model. Strategies and tools are described that help to alleviate this problem. These simplify the model-building process, quantify the goodness of fit of the model on a per-residue basis and locate possible errors in pep-tide and side-chain conformations.

durable goods. In such an economy, interest rates are determined by consumption loans between individuals of different ages. By introducing production employing a durable capital good into this model, one can examine the case where individuals provide for their retirement years by lending to entrepreneurs

Volatility permeates modern financial theories and decision making processes. As such, accurate measures and good forecasts of future volatility are critical for the implementation and evaluation of asset and derivative pricing theories as well as trading and hedging strategies. In response to this

We review accuracy estimation methods and compare the two most common methods: cross-validation and bootstrap. Recent experimental results on artificial data and theoretical results in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection

with frequent changes in individual prices) differs from inflation for "sticky-price goods" (those displaying infrequent price changes). Compared to the predictions of popular sticky price models, actual inflation rates are far more volatile and transient, particularly for sticky-price goods.

Marketing inherited a model of exchange from economics, which had a dominant logic based on the exchange of “goods, ” which usually are manufactured output. The dominant logic focused on tangible resources, embedded value, and transactions. Over the past several decades, new perspectives have

evaluation datasets and extensive comparison among methods. We show here how to train a conditional random field to achieve performance as good as any reported base noun-phrase chunking method on the CoNLL task, and better than any reported single model. Improved training methods based on modern

The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of three-dimensional models when an initial estimate of the relative pose is known. Many variants of ICP have been proposed, affecting all phases of the algorithm from the selection and matching of points

statistical modelling techniques. The size of the input block must be large (a few kilobytes) to achieve good compression.

the "discrete gamma model," uses several categories of rates to approximate the gamma distribution, with equal probability for each category. The mean of each category is used to represent all the rates falling in the category. The performance of this method is found to be quite good