'Independence assumption' diaporamas de présentation

Probabilistic Information Retrieval Approach for Ranking of Database Query Results

Probabilistic Information Retrieval Approach for Ranking of Database Query Results. Presenter: Ketaki Gadre. Authors: SURAJIT CHAUDHURI, GAUTAM DAS, VAGELIS HRISTIDIS, GERHARD WEIKUM . Introduction (1/4). Database Query Retrieval Model Boolean Model Many-Answers Problem:

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Horus: A WLAN-Based Indoor Location Determination System

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L11: Uses of Bayesian Networks

L11: Uses of Bayesian Networks. Nevin L. Zhang Department of Computer Science & Engineering The Hong Kong University of Science & Technology http://www.cse.ust.hk/~lzhang/. Outline. Traditional Uses Structure Discovery Density Estimation Classification Clustering An HKUST Project.

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Origins of Signal Detection Theory

Origins of Signal Detection Theory. Problem in Psychophysics Thresholds: is sensitivity discrete or continuous? Sensitivity confounded with response bias. Thresholds. Solution: detection theory (engineering ). Signal Detection Theory. Response. Yes. No. Signal.

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Information Retrieval (13)

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LINGUISTICA GENERALE E COMPUTAZIONALE

LINGUISTICA GENERALE E COMPUTAZIONALE. DISAMBIGUAZIONE DELLE PARTI DEL DISCORSO. POS tagging: the problem. People/NNS continue/VBP to/TO inquire/VB the/DT reason/NN for/IN the/DT race /NN for/IN outer/JJ space/NN Problem: assign a tag to race Requires: tagged corpus.

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Poisson Processes and Maximum Likelihood Estimator for Cache Replacement

Poisson Processes and Maximum Likelihood Estimator for Cache Replacement. A Preliminary Attempt ECEn 670 Semester Project Wei Dang Jacob Frogget. Outline. Motivation Cache r eplacement algorithms Poisson processes in webserver-based cache

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Deadline Miss Rates of Applications with Stochastic Task Execution Times

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Part V

Part V. From the Data at Hand to the World at Large. Chapter 18 Sampling Distribution Models. Modeling the Distribution of sample proportions From 1000 randomly selected voters (2004) Poll 1 : John Kerry 49% Poll 2 : John Kerry 45.9%. Assumptions and Conditions. Assumptions

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Confidence Intervals for Means

Confidence Intervals for Means. q uantitative data. means standard deviation. small ME. t. d f n-1. Normal model. fatter. increase. Independence assumption Random Sample Less than 10% of population Nearly normal (check with histogram).

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Effect of Violations of Normality

Effect of Violations of Normality. On the Correlation Coefficient t-Test. Edgell and Noon, 1984. Is the t-test for correlation coefficients robust to violations of its assumptions?. Overview. Review t-test of the Correlation Coefficient Violations Bivariate Normal Assumption

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Basics of regression analysis

Basics of regression analysis. Purpose of linear models Least-squares solution for linear models Analysis of diagnostics. Reason for linear models.

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LECTURE 28: HIERARCHICAL CLUSTERING

LECTURE 28: HIERARCHICAL CLUSTERING. Objectives: Unsupervised Bayesian Learning Similarity Measures Criterion Functions Iterative Optimization Agglomerative Clustering

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LECTURE 29: HIERARCHICAL CLUSTERING

LECTURE 29: HIERARCHICAL CLUSTERING. Objectives: Unsupervised Bayesian Learning Similarity Measures Criterion Functions Iterative Optimization Agglomerative Clustering

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Ch. 12 Inference for Proportions

Ch. 12 Inference for Proportions. 12.1 Inference for a proportion Sample Proportion. Section 12.2. Comparing two proportions. Comparing Two Proportions. Comparisons between two percentages are much more common than questions about isolated percentages. And they are more interesting.

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A Nonstationary Poisson View of Internet Traffic

A Nonstationary Poisson View of Internet Traffic. Thomas Karagiannis joint work with Mart Molle, Michalis Faloutsos, Andre Broido. What is the nature of Internet traffic?. The fundamental question How does Internet traffic look like? Two competing models

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Chapter 25: Paired Samples and Blocks

Chapter 25: Paired Samples and Blocks. AP Statistics. Paired/Matched Samples. This situation arises when your groups are NOT independent. What are common situations where this occurs Compare subjects with themselves before and after treatment

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Chapter 26

Chapter 26. Comparing Counts . Goodness-of-Fit. A test of whether the distribution of counts in one categorical variable matches the distribution predicted by a model is called a goodness-of-fit test.

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(X+Y) 2 =X 2 + Y 2 + 2 XY E (X+Y) 2 = E X 2 + E Y 2 + 2 E XY

Lecture 4 Rescaling, Sum and difference of random variables: simple algebra for mean and standard deviation. (X+Y) 2 =X 2 + Y 2 + 2 XY E (X+Y) 2 = E X 2 + E Y 2 + 2 E XY Var (X+Y) = Var (X) + Var (Y) if independence Demonstrate with Box model (computer simulation)

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