'Joint probability' diaporamas de présentation

Hidden Markov Models and Graphical Models

Hidden Markov Models and Graphical Models. CS294: Practical Machine Learning Oct. 8, 2009 Alex Simma (asimma@eecs) Based on slides by Erik Sudderth. Speech Recognition. Given an audio waveform, would like to robustly extract & recognize any spoken words Statistical models can be used to

1.17k views • 69 slides

P-Values

P-Values. Stephen Senn Competence Centre for Methodology and Statistics. Outline. Introduction to P_values History How they are defined and calculated What they are What they are not P-values as evidence? Distribution under null Distribution under alternative

1.23k views • 63 slides

Conditional Probability

1.38k views • 54 slides

Environmental Data Analysis with MatLab

Environmental Data Analysis with MatLab. Lecture 4: Multivariate Distributions. SYLLABUS.

750 views • 50 slides

Statistics

Statistics. Debra Kuyatt, MBA. Statistics Example: Stage 12 Tour de France, 2003. (Stage 12, 2003 Tour de France data is given on the slides at the end of this presentation.) Originally from: http://tdf.olntv.com. Definitions. Definitions. Population

613 views • 49 slides

A Primer on Machine Learning, Classification, and Privacy

869 views • 41 slides

Hidden Markov Models

602 views • 37 slides

SPM5 Segmentation

SPM5 Segmentation. A Growing Trend. Larger and more complex models are being produced to explain brain imaging data. Bigger and better computers allow more powerful models to be used More experience among software developers Older and wiser

553 views • 35 slides

A Survey of Probability Concepts

A Survey of Probability Concepts. Chapter 5. GOALS. Define probability. Describe the classical, empirical, and subjective approaches to probability. Explain the terms experiment, event, outcome, permutations, and combinations. Define the terms conditional probability and joint probability.

1.08k views • 31 slides

Multidimensional Scaling

Multidimensional Scaling. Agenda. Multidimensional Scaling Goodness of fit measures Nosofsky, 1986. Proximities. p Amherst, Hadley. Configuration (in 2-D). x i. Configuration (in 1-D). Formal MDS Definition. f: p ij ?d ij ( X )

764 views • 29 slides

A Review of CAT II/III LAAS Integrity Requirements and their Antecedents

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c 2 Test of Independence

308 views • 21 slides

Chapter 6 Probability

Chapter 6 Probability. Outline. Hypothesis Testing Review/experience Probability defined Probability and frequency tables Probability and the Normal Distribution Using the tables in the back. Hypothesis Testing. Type I/alpha, Type II/beta, Power/1-beta, 1-alpha, sigma, 1-sigma.

393 views • 18 slides

Bayes for Beginners

Bayes for Beginners. Presenters: Shuman ji & Nick Todd. Statistic Formulations. P(A): probability of event A occurring P(A|B): probability of A occurring given B occurred P(B|A): probability of B occurring given A occurred

503 views • 18 slides

Hidden Markov Models & Conditional Random Fields

Hidden Markov Models & Conditional Random Fields. Eran Segal Weizmann Institute. Representing Time. Add the time dimension to variables X ? X (t) Assumptions Time can be discretized into interesting points t 1 ,t 2 ,...t n Markov assumption holds

928 views • 16 slides

Maximum Likelihood

Maximum Likelihood. There are three major paradigms of estimating linear models Method of Moments Oldest estimation method Population moments are best estimated by sample moments Not too useful for complex estimation Least Squares Minimize the sum of the squared errors

827 views • 16 slides

Fundamental Statistics for the Behavioral Sciences, 5th edition David C. Howell

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Creating a story and a logical action plan from raw data

185 views • 8 slides

495 views • 8 slides

Management Science BUS 340

Management Science BUS 340 . Chapter 2 Problem 2-24 Presented by: Kelly Devilbiss. Boston South Fifth Street Softball League Win-Loss Record (past 5 years). a. What is the probability that the Killers will win every game next year?. 2/5 x 1/5 = 2/25 = 0.08

292 views • 6 slides