Methods for describing data

After studying this chapter you will be able to: Introduction to statistics, methods for describing data, probability, discrete probability distributions, the normal probability distribution, confidence interval, hypothesis testing.

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This course aims to provide student tools to conduct general ecological researches both in the field and laboratory. After successfully completing this course you should be able to: demonstrate understanding of procedure for planning an ecological research project; describe project design and data management in field research; Understand how to choose and describe the research sites; describe general sampling techniques for static and mobile organisms;...

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The object (for students) in this course is: To learn how to interpret statistical summaries appearing in journals, newspaper reports, internet, television, etc; to learn about the concepts of probability and probabilistic reasoning; to understand variability and analyze sampling distribution; to learn how to interpret and analyze data arising in your own work (course work or research).

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A Bayesian approach with frequentist validity has been developed to support inferences derived from a BLevel A^ in vivo-in vitro correlation (IVIVC). Irrespective of whether the in vivo data reflect in vivo dissolution or absorption, the IVIVC is typically assessed using a linear regression model. Confidence intervals are generally used to describe the uncertainty around the model. While the confidence intervals can describe population-level variability, it does not address the individual-level variability.

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An interface is a way to describe what classes should do, without specifying how they should do it. It’s not a class but a set of requirements for classes that want to conform to the interface E.g. public interface Comparable { int compareTo(Object otherObject); } this requires that any class implementing the Comparable interface contains a compareTo method, and this method must take an Object parameter and return an integer

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In this tutorial I will introduce ‘learning to rank’, a machine learning technology on constructing a model for ranking objects using training data. I will first explain the problem formulation of learning to rank, and relations between learning to rank and the other learning tasks. I will then describe learning to rank methods developed in recent years, including pointwise, pairwise, and listwise approaches.

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New technologies enable acquisition of large data-sets containing genomic, proteomic and metabolic information that describe the state of a cell. These data-sets call for systematic methods enabling relevant information about the inner workings of the cell to be extracted. One important issue at hand is the understanding of the functional interactions between genes, proteins and metabolites.

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The number of macromolecular structures deposited in the Protein Data Bank now exceeds 45 000, with the vast majority determined using crystal-lographic methods. Thousands of studies describing such structures have been published in the scientific literature, and 14 Nobel prizes in chemistry or medicine have been awarded to protein crystallographers.

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