'Stationary process' presentation slideshows

Sequence Scoring Experiments Using the TIMIT Corpus and the HTK Recognition Framework

1.55k views • 110 slides

Chapter 4 Random Processes

Chapter 4 Random Processes. 4.2 Random processes : Basic concepts 4.2.1Description of Random Process. Def : for each S , we assign a time function depicted by X (t, ) or . (i.e. we assign a function X (t, ) to every outcome )

1.73k views • 72 slides

Minimum Classification Error (MCE) Approach in Pattern Recognition

Minimum Classification Error (MCE) Approach in Pattern Recognition. Wu Chou, Avaya Labs Research, Avaya Inc., USA. Outline (1/2). Introduction Optimal Classifier from Bayes Desicion Theory Discriminant Function Approach to Classifier Design Speech Recogniation and Hidden Markov Modeling

783 views • 53 slides

Stochastic Processes

Stochastic Processes. Elements of Stochastic Processes Lecture II. Overview. Reading Assignment Chapter 9 of textbook Further Resources MIT Open Course Ware S. Karlin and H. M. Taylor, A First Course in Stochastic Processes , 2nd ed., Academic Press, New York, 1975. Outline.

1.7k views • 41 slides

Which Model Should Be Appropriate to Describe the Dynamics of Trading Duration in Chinese Stock Market?

Which Model Should Be Appropriate to Describe the Dynamics of Trading Duration in Chinese Stock Market?. Wanbo Lu luwb@swufe.edu.cn School of Statistics, Southwestern University of Finance and Economics, 55 Guanghua Village Road, Chengdu, 610074, China. Outline. 1. Introduction

619 views • 37 slides

Advanced signal processing Dr. Mohamad KAHLIL Islamic University of Lebanon

Advanced signal processing Dr. Mohamad KAHLIL Islamic University of Lebanon. Outline. Random variables Histogram, Mean, Variances, Moments, Correlation, types, multiple random variables Random functions Correlation, stationarity, spectral density estimation methods

601 views • 35 slides

Rethink on the inference of annual maximum wind speed distribution

The 4 th Conference on Extreme Value Analysis: Probabilistic and statistical models and their applications, Gothenburg, 2005. Rethink on the inference of annual maximum wind speed distribution. Hang CHOI GS Engineering & Construction Corp., Seoul, KOREA number3choi@unitel.co.kr Jun KANDA

573 views • 32 slides

10 Further Time Series OLS Issues

10 Further Time Series OLS Issues. Chapter 10 covered OLS properties for finite (small) sample time series data -If our Chapter 10 assumptions fail, we need to derive large sample time series data OLS properties -for example, if strict exogeneity fails (TS.3)

326 views • 32 slides

Groundwater permeability

Groundwater permeability. Easy to solve the forward problem: flow of groundwater given permeability of aquifer Inverse problem: determine permeability from flow (usually of tracers) With some models enough to look at first arrival of tracer at each well (breakthrough times). Notation.

375 views • 29 slides

Automatic speech recognition using an echo state network

Automatic speech recognition using an echo state network. Mark D. Skowronski Computational Neuro-Engineering Lab Electrical and Computer Engineering University of Florida, Gainesville, FL, USA May 10, 2006. 2000. CNEL Seminar History. Ratio spectrum, Oct. 2000 HFCC, Sept. 2002

485 views • 26 slides

Stationarity, Non Stationarity, Unit Roots and Spurious Regression

Stationarity, Non Stationarity, Unit Roots and Spurious Regression. Roger Perman Applied Econometrics Lecture 11. Stationary Time Series. Exhibits mean reversion in that it fluctuates around a constant long run mean Has a finite variance that is time invariant

504 views • 26 slides

Minimum Mean Squared Error Time Series Classification Using an Echo State Network Prediction Model

Minimum Mean Squared Error Time Series Classification Using an Echo State Network Prediction Model. Mark Skowronski and John Harris Computational Neuro-Engineering Lab University of Florida. Automatic Speech Recognition Using an Echo State Network. Mark Skowronski and John Harris

349 views • 20 slides

Applied Econometrics 31 456

Applied Econometrics 31 456. This half of the course provides an introduction to stationary time series data. Nobel Prize for Economics in 2003 awarded to Rob Engle and Clive Granger, who highlighted the importance of stationarity in time series data.

727 views • 18 slides

316 views • 18 slides

Workshop 2: Spatial scale and dependence in biogeographical patterns

Workshop 2: Spatial scale and dependence in biogeographical patterns. Objective: introduce the following fundamental concepts on spatial data analysis: - What is spatial data analysis and why it is important - Pattern vs process

336 views • 18 slides

Workshop 2: Spatial scale and dependence in biogeographical patterns

Workshop 2: Spatial scale and dependence in biogeographical patterns. Objective: introduce the following fundamental concepts on spatial data analysis: - What is spatial data analysis and why it is important - Pattern vs process

196 views • 18 slides

Stochastic Process - Introduction

Stochastic Process - Introduction. Stochastic processes are processes that proceed randomly in time. Rather than consider fixed random variables X, Y , etc. or even sequences of i.i.d random variables, we consider sequences X 0 , X 1 ,

221 views • 16 slides

Probabilistic Reasoning over Time

407 views • 14 slides

Probabilistic Reasoning over Time

174 views • 14 slides

240 views • 11 slides