Statistical hypotheses

(BQ) Part 1 book "Design of experiments in chemical engineering" has contents: The simplest discrete and continuous distributions, discrete distributions, continuous distribution, normal distributions, statistical inference, statistical hypotheses, statistical estimation,...and other contents.
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We introduce a novel search algorithm for statistical machine translation based on dynamic programming (DP). During the search process two statistical knowledge sources are combined: a translation model and a bigram language model. This search algorithm expands hypotheses along the positions of the target string while guaranteeing progressive coverage of the words in the source string. We present experimental results on the Verbmobil task.
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(BQ) Part 2 book "Introduction to probability and statistics" has contents: LargeSample tests of hypotheses; inference from small samples; the analysis of variance; linear regression and correlation; multiple regression analysis; analysis of categorical data; nonparametric statistics.
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This paper presents hypothesis mixture decoding (HM decoding), a new decoding scheme that performs translation reconstruction using hypotheses generated by multiple translation systems.
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We describe Akamon, an open source toolkit for tree and forestbased statistical machine translation (Liu et al., 2006; Mi et al., 2008; Mi and Huang, 2008). Akamon implements all of the algorithms required for tree/foresttostring decoding using treetostring translation rules: multiplethread forestbased decoding, ngram language model integration, beam and cubepruning, kbest hypotheses extraction, and minimum error rate training.
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Word and ngram posterior probabilities estimated on Nbest hypotheses have been used to improve the performance of statistical machine translation (SMT) in a rescoring framework. In this paper, we extend the idea to estimate the posterior probabilities on Nbest hypotheses for translation phrasepairs, target language ngrams, and source word reorderings. The SMT system is selfenhanced with the posterior knowledge learned from Nbest hypotheses in a redecoding framework.
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This paper describes a novel method for computing a consensus translation from the outputs of multiple machine translation (MT) systems. The outputs are combined and a possibly new translation hypothesis can be generated. Similarly to the wellestablished ROVER approach of (Fiscus, 1997) for combining speech recognition hypotheses, the consensus translation is computed by voting on a confusion network.
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Chapter 9  Hypothesis testing. After mastering the material in this chapter, you will be able to: Set Up appropriate null and alternative hypotheses, describe Type I and Type II errors and their probabilities, use critical values and pvalues to perform a z test about a population mean when s is known,...
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Chapter 13  Chisquare tests. After mastering the material in this chapter, you will be able to: Test hypotheses about multinomial probabilities by using a chisquare goodnessoffit test, perform a goodnessoffit test for normality, decide whether two qualitative variables are independent by using a chisquare test for independence.
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Lecture "Applied econometrics course  Chapter 3: Statistic inference and hypothesis testing" has content: The distribution of the parameters, hypotheses testing, testing multiple linear restrictions,... and other contents.
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(BQ) Part 2 book "Statistics" has contents: Inferences based on a two samples  Confidence intervals and tests of hypotheses; analysis of variance  Comparing more than two means; simple linear regression; multiple regression and model building; categorical data analysis; nonparametric statistics.
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his monograph presents methods for full comparative distributional analysis based on the relative distribution. This provides a general integrated framework for analysis, a graphical component that simplifies exploratory data analysis and display, a statistically valid basis for the development of hypothesisdriven summary measures, and the potential for decomposition  enabling the examination of complex hypotheses regarding the origins of distributional changes within and between groups.
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As science, ecology is often accused of being weak because of its basic lack of predictive power (Peters 1991) and the many ecological concepts judged vague or tautological (ShraderFrechette and McCoy 1993). Also, important paradigms that dominated the ecological scene for years have been discarded in favor of new concepts and theories that swamp the most recent ecological literature (e.g., the abandoning of the island biogeography theory in favor of the metapopulations theory; Hanski and Simberloff 1997).
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In this chapter you will: Develop an understanding of the importance and nature of quality control checks, understand the data entry process and data entry alternatives, learn how surveys are tabulated and crosstabulated, understand the concept of hypothesis development and how to text hypotheses.
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Chapter 7 – Hypothesis testing. This chapter include objectives: Define a hypothesis and describe the steps of hypothesis testing, define a hypothesis and describe the steps of hypothesis testing, distinguish between onetailed and twotailed tests of hypotheses,...
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In this chapter students will be able to: Explain the difference between descriptive and inferential statistics; use the four analytical steps to interpret written research findings; identify if the appropriate test of difference is used with research questions and hypotheses; apart from the researcher's written presentation, independently interpret research findings.
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C H A P T E R 17 High performance – statistical inference for comparing population means and bivariate data Chapter objectives This chapter will help you to: ■ test hypotheses on the difference between two population means using independent samples and draw appropriate conclusions ■ carry out tests of hypotheses about the difference between two population means using paired data and draw appropriate conclusions ■ test differences between population means using analysis of variance analysis (ANOVA) and draw appropriate conclusions ...
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Generalizing a Sample’s Findings to Its Population and Testing Hypotheses About Percents and Means. Statistics Versus Parameters • Statistics: values that are computed from information provided by a sample • Parameters: values that are computed from a complete census which are considered to be precise and valid measures of the population
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Detection and classification arise in signal processing problems whenever a decision is to be made among a finite number of hypotheses concerning an observed waveform. Signal detection algorithms decide whether the waveform consists of “noise alone” or “signal masked by noise.” Signal classification algorithms decide whether a detected signal belongs to one or another of prespecified classes of signals. The objective of signal detection and classification theory is to specify systematic strategies for designing algorithms which minimize the average number of decision errors.
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This difference is substantial and highly statistically significant in all specifications. These results are consistent with two different hypotheses. First, unobservable factors at the management company level could be associated with both the decision to specialize in SRI funds and higher fees and performance. In this case, socially responsible investing itself would not have any effect on performance or fees.
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