These are my links for April 26th through April 30th:
- DoCIS: Documents in Computer and Library & Information Science – DoCIS is a service of the rclis digital library. rclis is dedicated to promoting free access to data about documents in computing and library and information science.DoCIS provides an integrated browsing/searching interface to rclis data. To start browsing our collection, please look at the list of journals and conference proceedings that we cover.
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- Mathematical Problems by David Hilbert – The supply of problems in mathematics is inexhaustible, and as soon as one problem is solved numerous others come forth in its place. Permit me in the following, tentatively as it were, to mention particular definite problems, drawn from various branches of mathematics, from the discussion of which an advancement of science may be expected.
Let us look at the principles of analysis and geometry. The most suggestive and notable achievements of the last century in this field are, as it seems to me, the arithmetical formulation of the concept of the continuum in the works of Cauchy, Bolzano and Cantor, and the discovery of non-euclidean geometry by Gauss, Bolyai, and Lobachevsky. I therefore first direct your attention to some problems belonging to these fields.
- Institute of Mathematical Statistics Lecture Notes – Monograph Series – Free collection of mathematical statistics monographs.
- MathFiction – Of the many works of fiction that are published, very few involve mathematics or mathematicians. However, people who like mathematics (or are mathematicians ourselves) may especially enjoy reading those few that do. Moreover, as I argue in an article in the AMS Notices, mathematicians should be interested in these works of “mathematical fiction” even if we do not enjoy them because they both affect and reflect the non-mathematician’s view of this subject.
These are my links for December 14th through January 5th:
These are my links for September 22nd through September 25th:
- Miniature Pearl: Causal Inference in Statistics: An Overview”, forthcoming in Statistics Surveys 3 (2009): 96–146 – This review presents empirical researchers with recent advances in causal inference, and stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals…
These are my links for September 14th through September 22nd:
- Philosophy Now | Daniel Dennett: Autobiography (Part 1) – What makes a philosopher? In the first of a two-part mini-epic, Daniel C. Dennett contemplates a life of the mind – his own. Part 1: The pre-professional years.
- Philosopher’s Annual – Our goal is to select the ten best articles published in philosophy each year—an attempt as simple to state as it is admittedly impossible to fulfill. Against a background of twenty-four volumes in hard copy, the Annual is now available entirely online.
- Revolutions: Interactive stock visualizations with R – Jeroen Ooms, who recently completed his Masters in Statistics at Utrech University, has created an outstanding web-based drag-and-drop application for visualizing financial data. With his “StockPlot” t application, you can select any stock from a number of world exchanges (including NASDAQ, DAX, FTSE), and drag it to a worksheet to see a time-series of the stock price. You can arrange up to four charts on the same worksheet for comparison purposes, and control the timeframe and appearance of each chart.
- Revolutions: Machine Learning in R, in a nutshell – Josh Reich has created a concise R script demonstrating various machine-learning techniques in R with simple, self-contained examples.
- Information Processing and Thermodynamic Entropy (Stanford Encyclopedia of Philosophy) – Are principles of information processing necessary to demonstrate the consistency of statistical mechanics? Does the physical implementation of a computational operation have a fundamental thermodynamic cost, purely by virtue of its logical properties? These two questions lie at the centre of a large body of literature concerned with the Szilard engine (a variant of the Maxwell’s demon thought experiment), Landauer’s principle (supposed to embody the fundamental principle of the thermodynamics of computation) and possible connections between the two. A variety of attempts to answer these questions have illustrated many open questions in the foundations of statistical mechanics.
- Christopher J. G. Meacham, Two Mistakes Regarding The Principal Principle | PhilPapers – This paper examines two mistakes regarding David Lewis’ Principal Principle that have appeared in the recent literature. These particular mistakes are worth looking at for several reasons: the thoughts that lead to these mistakes are natural ones, the principles that result from these mistakes are untenable, and these mistakes have led to significant misconceptions regarding the role of admissibility and time. After correcting these mistakes, the paper discusses the correct roles of time and admissibility. With these results in hand, the paper concludes by showing that one way of formulating the chance-credence relation has a distinct advantage over its rivals.
- José Luis Bermúdez – Decision Theory and Rationality – Reviewed by Lara Buchak, UC Berkeley – Philosophical Reviews – University of Notre Dame – Decision theory is used for a variety of purposes: decision makers use it to guide their own actions, and theorists use it both normatively to assess decision makers and to predict and explain their decisions. This book investigates whether the theory can fulfill all three of these purposes. In particular, Bermúdez explores three questions that decision theory must answer under any guise: How should we understand utility and preference? How finely should we individuate the possible outcomes in a decision problem? And how should choice be constrained over time? He argues that there are no answers to these questions that allow decision theory to serve all three purposes.
These are my links for July 6th through July 8th:
- How to choose a statistical test – This book has discussed many different statistical tests. To select the right test, ask yourself two questions: What kind of data have you collected? What is your goal? Then refer to Table 37.1.
- NPWRC :: Statistical Significance Testing – Four basic steps constitute statistical hypothesis testing. First, one develops a null hypothesis about some phenomenon or parameter. This null hypothesis is generally the opposite of the research hypothesis, which is what the investigator truly believes and wants to demonstrate. Research hypotheses may be generated either inductively, from a study of observations already made, or deductively, deriving from theory. Next, data are collected that bear on the issue, typically by an experiment or by sampling. (Null hypotheses often are developed after the data are in hand and have been rummaged through, but that’s another topic.)
- Data Mining Techniques – Data Mining is an analytic process designed to explore data (usually large amounts of data – typically business or market related) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. The ultimate goal of data mining is prediction – and predictive data mining is the most common type of data mining and one that has the most direct business applications.
- An Overview of Data Mining Techniques – This overview provides a description of some of the most common data mining algorithms in use today. We have broken the discussion into two sections, each with a specific theme:* Classical Techniques: Statistics, Neighborhoods and Clustering
* Next Generation Techniques: Trees, Networks and Rules
Each section will describe a number of data mining algorithms at a high level, focusing on the “big picture” so that the reader will be able to understand how each algorithm fits into the landscape of data mining techniques. Overall, six broad classes of data mining algorithms are covered. Although there are a number of other algorithms and many variations of the techniques described, one of the algorithms from this group of six is almost always used in real world deployments of data mining systems.
- MachineLearning.pdf (application/pdf Object) – Over the past 50 years the study of Machine Learning has grown from the efforts of a handful of computer engineers exploring whether computers could learn to play games, and a field of Statistics that largely ignored computational considerations, to a broad discipline that has produced fundamental statistical-computational theories of learning processes, has designed learning algorithms that are routinely used in commercial systems
for speech recognition, computer vision, and a variety of other tasks, and has spun off an industry in data mining to discover hidden regularities in the growing volumes of online data. This document provides a brief and personal view of the discipline that has emerged as Machine Learning, the fundamental questions it addresses, its relationship to other sciences and society, and where it might be headed
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