'Parameter estimation' diaporamas de présentation

Learning Bayesian Networks from Data

Learning Bayesian Networks from Data. Nir Friedman Daphne Koller Hebrew U. Stanford . Overview. Introduction Parameter Estimation Model Selection Structure Discovery Incomplete Data Learning from Structured Data. Qualitative part :

1.39k views • 113 slides

Missing Data: Analysis and Design

Missing Data: Analysis and Design. John W. Graham The Prevention Research Center and Department of Biobehavioral Health Penn State University. Presentation in Four Parts. (1) Introduction: Missing Data Theory (2) A brief analysis demonstration Multiple Imputation with NORM and Proc MI

2.28k views • 110 slides

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning. Chapter 2: Probability distributions. Parametric Distributions. Basic building blocks: Need to determine given Representation: or ? Recall Curve Fitting. Binary Variables (1). Coin flipping: heads=1, tails=0

1.28k views • 86 slides

Parameter Estimation and Performance Analysis of Several Network Applications

Parameter Estimation and Performance Analysis of Several Network Applications. Sara Alouf Ph.D. defense - November 8, 2002. Advisor: Philippe Nain. Thesis topics. Adaptive unicast applications Background: network does not offer guarantee Objective: estimate network internal state.

911 views • 75 slides

Statistical Methods in Particle Physics Day 1: Introduction

969 views • 63 slides

Dynamic Causal Modelling (DCM ) for fMRI

Dynamic Causal Modelling (DCM ) for fMRI. Rosalyn Moran Virginia Tech Carilion Research Institute With thanks to the FIL Methods Group for slides and images. SPM Course, UCL May 2013. Dynamic causal modelling (DCM).

683 views • 51 slides

COMM 1004: Detection & Estimation

Prof. Ahmed El-Mahdy Dean of the faculty of IET The German University in Cairo. COMM 1004: Detection & Estimation. H.L. Van Trees, Detection, Estimation, and Linear Modulation Theory, vol. I. John Wiley& sons, New York, 2001.

893 views • 40 slides

MODIS Data Assimilation at GMAO

MODIS Data Assimilation at GMAO. Arlindo da Silva Global Modeling and Assimilation Office NASA/Goddard Space Flight Center Peter Norris, Lars-Peter Riishojgaard , GMAO Pete Colarco, Clark Weaver, Mian Chin, Code 613.3. Outline. Assimilation of cloud observations: ISCCP MODIS

579 views • 37 slides

MODIS Data Assimilation at GMAO

MODIS Data Assimilation at GMAO. Arlindo da Silva Global Modeling and Assimilation Office NASA/Goddard Space Flight Center Peter Norris, Lars-Peter Riishojgaard , GMAO Pete Colarco, Clark Weaver, Mian Chin, Code 613.3. Outline. Assimilation of cloud observations: ISCCP MODIS

614 views • 37 slides

Models for the Analysis of Discrete Compositional Data An Application of Random Effects Graphical Models

Models for the Analysis of Discrete Compositional Data An Application of Random Effects Graphical Models. Devin S. Johnson STARMAP Department of Statistics Colorado State University Developed under the EPA STAR Research Assistance Agreement CR-829095. Dissertation Contributions.

665 views • 31 slides

A Probabilistic Approach to Spatiotemporal Theme Pattern Mining on Weblogs

488 views • 23 slides

Profile Hidden Markov Models

Profile Hidden Markov Models . Bioinformatics Fall-2004 Dr Webb Miller and Dr Claude Depamphilis Dhiraj Joshi Department of Computer Science and Engineering The Pennsylvania State University. Outline. Introduction to HMMs Profile HMMs

551 views • 23 slides

Innovative Algorithm to Estimate Scalable Parameters for Gas Mobility Control Simulation

19 th Consortium. Innovative Algorithm to Estimate Scalable Parameters for Gas Mobility Control Simulation. Yongchao Zeng Aarthi Muthuswamy, Maura Puerto, Kun Ma ? , Ying (Annie) Wang, Sibani L. Biswal * and George J. Hirasaki *

226 views • 22 slides

Tyre and Vehicle Model Identification using Identifying Kalman Filters

Tyre and Vehicle Model Identification using Identifying Kalman Filters. Matthew C Best. Department of Aeronautical and Automotive Engineering Loughborough University, UK. Background. Kalman filters are used widely for state estimation, but rely on good, suitably simple vehicle / tyre models

437 views • 19 slides

Simultaneous Image Classification and Annotation

(Final Version). Simultaneous Image Classification and Annotation. Chong Wang, David Blei, Li Fei-Fei Computer Science Department Princeton University Published in CVPR 2009 Presented by Eric Wang 7-3-09. Outline. Introduction Review of sLDA and Corr -LDA Model description

388 views • 19 slides

Lecture 7

Lecture 7. Last day: 2.6 and 2.7 Today: 2.8 and begin 3.1-3.2 Next day: 3.3-3.5 Assignment #2: Chapter 2: 6, 15 (treat tape speed and laser power as qualitative factors), 27, 30, 32, and 36. Balanced Incomplete Block Designs. Sometimes cannot run all treatments in each block

509 views • 18 slides

Stochastic Models For Heterogeneous DNA Sequences

314 views • 17 slides

Random sampling and probability

Random sampling and probability. Introduction to inferential statistics. Inferential statistics. Review definition: Making a decision about what you do not know -- the population characteristics -- based on what you do know -- the sample data. Two purposes of inferential statistics:

490 views • 16 slides

Hidden Markov Models - Training

Hidden Markov Models - Training . Parameter Estimation. How to specify a HMM mode? Design the structure: states, transitions, etc. Assign the parameter values: the transition and emission probabilities a kl and e k (b). Parameter Estimation.

390 views • 16 slides

The General Linear Model

399 views • 13 slides