Many people find statistics challenging, but most statistics professors do not.
As a result, it is sometimes hard for our professors and the authors of statistics
textbooks to make statistics clear and practical for business students,
managers, and executives. Business Statistics Demystified fills that gap. We
begin slowly, introducing statistical concepts without mathematics.
When sitting in statistics classes or when trying to read and understand
statistical material, too many otherwise intelligent and capable students and
researchers feel dumb. This book is intended as an antidote. It is designed to
make you feel smart and competent. Its approach is conservative in that it
attempts to identify and present the essentials of data analysis as developed by
statisticians over the last two or three centuries.
CHAPTER 1 Preface. These are class notes from several different graduate econometrics and statistics classes. In the Spring 2000 they were used for Statistics 6869, syllabus on p. ??, and in the Fall 2000 for Economics 7800, syllabus on p. ??.
We present a syntax-based statistical translation model. Our model transforms a source-language parse tree into a target-language string by applying stochastic operations at each node. These operations capture linguistic differences such as word order and case marking. Model parameters are estimated in polynomial time using an EM algorithm. The model produces word alignments that are better than those produced by IBM Model 5. is conditioned only on word classes and positions in the string, and the duplication and translation are conditioned only on the word identity. ...
I am grateful for the contributions that many people have made to this
book. Ed Maggin was the first to teach me Statistical Thermodynam-ics and his class notes were always a point of reference. The late Ted
H. Davis gave me encouragement and invaluable feedback. Dan Bolin-tineanu and Thomas Jikku read the final draft and helped me make many
corrections. Many thanks go to the students who attended my course in
Statistical Thermodynamics and who provided me with many valuable
comments regarding the structure of the book.
In this paper, we present a novel approach which incorporates the web-derived selectional preferences to improve statistical dependency parsing. Conventional selectional preference learning methods have usually focused on word-to-class relations, e.g., a verb selects as its subject a given nominal class.
In this paper, a new language model, the Multi-Class Composite N-gram, is proposed to avoid a data sparseness problem for spoken language in that it is difﬁcult to collect training data. The Multi-Class Composite N-gram maintains an accurate word prediction capability and reliability for sparse data with a compact model size based on multiple word clusters, called MultiClasses. In the Multi-Class, the statistical connectivity at each position of the N-grams is regarded as word attributes, and one word cluster each is created to represent the positional attributes. ...
This paper describes an all level approach on statistical natural language translation (SNLT). W i t h o u t any predefined knowledge the system learns a statistical translation lexicon (STL), word classes (WCs) and translation rules (TRs) from a parallel corpus thereby producing a generalized form of a word alignment (WA). The translation process itself is realized as a beam search.
In this paper I propose a generalization of lexical association techniques that is intended to facilitate statistical discovery of facts involving word classes rather than individual words. Although defining association measures over classes (as sets of words) is straightforward in theory, making direct use of such a definition is impractical because there are simply too many classes to consider. Rather than considering all possible classes, I propose constraining the set of possible word classes by using a broad-coverage lexical/conceptual hierarchy [Miller, 1990]. ...
A serious bottleneck of comparative parser evaluation is the fact that different parsers subscribe to different formal frameworks and theoretical assumptions. Converting outputs from one framework to another is less than optimal as it easily introduces noise into the process. Here we present a principled protocol for evaluating parsing results across frameworks based on function trees, tree generalization and edit distance metrics. This extends a previously proposed framework for cross-theory evaluation and allows us to compare a wider class of parsers. ...
In statistical natural language processing we always face the problem of sparse data. One way to reduce this problem is to group words into equivalence classes which is a standard method in statistical language modeling. In this paper we describe a method to determine bilingual word classes suitable for statistical machine translation. We develop an optimization criterion based on a maximumlikelihood approach and describe a clustering algorithm. We will show that the usage of the bilingual word classes we get can improve statistical machine translation. ...
The institution of a leisure class is found in its best development at the higher stages of the barbarian culture; as, for instance, in feudal Europe or feudal Japan. In such communities the distinction between classes is very rigorously observed; and the feature of most striking economic significance in these class differences is the distinction maintained between the employments proper to the several classes. The upper classes are by custom exempt or excluded from industrial occupations, and are reserved for certain employments to which a degree of honour attaches.
In statistical language modeling, one technique to reduce the problematic eﬀects of data sparsity is to partition the vocabulary into equivalence classes. In this paper we investigate the eﬀects of applying such a technique to higherorder n-gram models trained on large corpora.
This paper examines whether a learningbased coreference resolver can be improved using semantic class knowledge that is automatically acquired from a version of the Penn Treebank in which the noun phrases are labeled with their semantic classes. Experiments on the ACE test data show that a resolver that employs such induced semantic class knowledge yields a statistically significant improvement of 2% in F-measure over one that exploits heuristically computed semantic class knowledge. In addition, the induced knowledge improves the accuracy of common noun resolution by 2-6%.
INTENDED FOR CLASS USE OR SELF-STUDY, this text aspires to introduce statistical
methodology to a wide audience, simply and intuitively, through
resampling from the data at hand.
The resampling methods—permutations and the bootstrap—are easy to
learn and easy to apply. They require no mathematics beyond introductory
high-school algebra, yet are applicable in an exceptionally broad range of
Asset allocation investigates the optimal division of a portfolio among different asset
classes. Standard theory involves the optimal mix of risky stocks, bonds, and cash
together with various subdivisions of these asset classes. Underlying this is the insight
that diversification allows for achieving a balance between risk and return: by using
different types of investment, losses may be limited and returns are made less volatile
without losing too much potential gain.
Statistical communication theory is generally ragarded as having been founed by Shannon( 1948) and Wiene(1949), who conceived of the communication situation as one in which a signal chosen from a specified class is to be tranmitted through of channel, but the output of the channel is not determined by the input
Probability and Statistics for Programmers
Probability and Statistics for Programmers
Allen B. Downey Green Tea Press
.Green Tea Press 9 Washburn Ave Needham MA 02492
Permission is granted to copy, distribute, and/or modify this document under the terms of the Creative Commons Attribution-NonCommercial 3.0 Unported License, which is available at http://creativecommons.org/licenses/by-nc/3.0/.
CHAPTER 3 Random Variables. 3.1. Notation Throughout these class notes, lower case bold letters will be used for vectors and upper case bold letters for matrices, and letters that are not bold for scalars. The (i, j) element of the matrix A is aij , and the ith element of a vector b is bi
Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011, Article ID 980805, 15 pages doi:10.1155/2011/980805
Research Article Optimal Multitaper Wigner Spectrum Estimation of a Class of Locally Stationary Processes Using Hermite Functions
Maria Hansson-Sandsten (EURASIP Member)
Mathematical Statistics, Centre for Mathematical Sciences, Lund University, P.O. Box 118, 221 00 Lund, Sweden Correspondence should be addressed to Maria Hansson-Sandsten, firstname.lastname@example.org.