'Maximum likelihood estimate' diaporamas de présentation

SPH6004 Advanced Biostatistics

SPH6004 Advanced Biostatistics. Part 1: Bayesian Statistics Chapter 1: Introduction to Bayesian Statistics. Golden rule: please stop me to ask questions. Objectives. Describe differences between Bayesian and classical statistics

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LING / C SC 439/539 Statistical Natural Language Processing

LING / C SC 439/539 Statistical Natural Language Processing. Lecture 15 3 /2/2013. Recommended reading. Jurafsky & Martin Chapter 5 through 5.7, Part of Speech Tagging Chapter 6 through 6.5, Hidden Markov Models Toutanova & Manning (2000): error analysis for POS tagging. Outline.

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RNA seq (I)

RNA seq (I). Edouard Severing. A typical heat stress experiment ( climate change ). Economically important frog. Heat stress ( convection ). Control. 85 minutes. How does the frog adapt and survive?. 5 days. Coping with heat stress.

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Fundamental Building Blocks of Social Structure

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Linear Classification Models: Generative

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Ranking Systems: Manipulability and Efficiency

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Ngram models and the Sparsity problem

Ngram models and the Sparsity problem. John Goldsmith November 2002. The task. Find a probability distribution for the current word in a text (utterance, etc.), given what the last n words have been. (n = 0,1,2,3) Why this is reasonable What the problems are . Why this is reasonable.

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Maximum likelihood estimation

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Introduction to Mantel-Haenszel estimate

Introduction to Mantel-Haenszel estimate .

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Chapter 7. Statistical Estimation and Sampling Distributions

Chapter 7. Statistical Estimation and Sampling Distributions. 7.1 Point Estimates 7.2 Properties of Point Estimates 7.3 Sampling Distributions 7.4 Constructing Parameter Estimates 7.5 Supplementary Problems. 7.1 Point Estimates 7.1.1 Parameters. Parameters

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Computing Kemeny and Slater Rankings Vincent Conitzer (Joint work with Andrew Davenport and Jayant Kalagnanam at IBM Res

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Chapter 6 Kernel Smoothing Methods

Chapter 6 Kernel Smoothing Methods. -Zhang Yuxin. One-Dimensional Kernel Smoothers. Review the K-nearest-neighbor average method in Chapter 2. Use the 30-nearest neighborhood. An example. Nadaraya -Watson kernel-weighted average. Nadaraya -Watson kernel-weighted average.

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Ngram models and the Sparsity problem

Ngram models and the Sparsity problem. John Goldsmith. The task. Find a probability distribution for the current word in a text (utterance, etc.), given what the last n words have been. (n = 0,1,2,3) Why this is reasonable What the problems are . Why this is important.

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Missing variable problems

Missing variable problems. In many vision problems, if some variables were known the maximum likelihood inference problem would be easy fitting; if we knew which line each token came from, it would be easy to determine line parameters

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Ngram models and the Sparcity problem

Ngram models and the Sparcity problem. The task. Find a probability distribution for the current word in a text (utterance, etc.), given what the last n words have been. (n = 0,1,2,3) Why this is reasonable What the problems are . Why this is reasonable.

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Machine Translation and MT tools: Giza++ and Moses

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BMS 617

BMS 617. Lecture 11: Models. What is a model?. In general, a model is a (simpler) representation of something else We use models to study complex phenomena Easier to manipulate than the real thing of interest Easier to focus on specific aspects

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BASIC NOTIONS OF PROBABILITY THEORY

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