Density Surfaces Arturas Mazeika. Outline of the Presentation A very brief overview of the architecture of the 3DVDM system An intuition of the density.

Slides:

Advertisements

Similar presentations

Brief introduction on Logistic Regression

Advertisements

Hypothesis testing and confidence intervals by resampling by J. Kárász.

SAMPLE DESIGN: HOW MANY WILL BE IN THE SAMPLE—DESCRIPTIVE STUDIES ?

Partially Observable Markov Decision Process (POMDP)

Chapter 6 Sampling and Sampling Distributions

Yicheng Tu, § Shaoping Chen, §¥ and Sagar Pandit § § University of South Florida, Tampa, Florida, USA ¥ Wuhan University of Technology, Wuhan, Hubei, China.

FTP Biostatistics II Model parameter estimations: Confronting models with measurements.

Model generalization Test error Bias, variance and complexity

PROBABILISTIC ASSESSMENT OF THE QSAR APPLICATION DOMAIN Nina Jeliazkova 1, Joanna Jaworska 2 (1) IPP, Bulgarian Academy of Sciences, Sofia, Bulgaria (2)

Christine Smyth and Jim Mori Disaster Prevention Research Institute, Kyoto University.

Copyright ©2013 by SJTU, IWCT. Dongchuan Road #800, Minhang, Shanghai, All rights reserved. Indoor Localization with a Crowdsourcing based Fingerprints.

University of Wisconsin-Milwaukee Geographic Information Science Geography 625 Intermediate Geographic Information Science Instructor: Changshan Wu Department.

Overview of Non-Parametric Probability Density Estimation Methods Sherry Towers State University of New York at Stony Brook.

Self-Organizing Hierarchical Neural Network

Estimation from Samples Find a likely range of values for a population parameter (e.g. average, %) Find a likely range of values for a population parameter.

Chapter 7 Sampling and Sampling Distributions

Adaptive Rao-Blackwellized Particle Filter and It’s Evaluation for Tracking in Surveillance Xinyu Xu and Baoxin Li, Senior Member, IEEE.

Lecture 8 The Principle of Maximum Likelihood. Syllabus Lecture 01Describing Inverse Problems Lecture 02Probability and Measurement Error, Part 1 Lecture.

Probability Grid: A Location Estimation Scheme for Wireless Sensor Networks Presented by cychen Date ： 3/7 In Secon (Sensor and Ad Hoc Communications and.

Today Logistic Regression Decision Trees Redux Graphical Models

Review of normal distribution. Exercise Solution.

University of Wisconsin-Milwaukee Geographic Information Science Geography 625 Intermediate Geographic Information Science Instructor: Changshan Wu Department.

What We Know So Far… Data plots or graphs

Sampling: Theory and Methods

Dr. Richard Young Optronic Laboratories, Inc..  Uncertainty budgets are a growing requirement of measurements.  Multiple measurements are generally.

Target Tracking with Binary Proximity Sensors: Fundamental Limits, Minimal Descriptions, and Algorithms N. Shrivastava, R. Mudumbai, U. Madhow, and S.

Chapter Nine Copyright © 2006 McGraw-Hill/Irwin Sampling: Theory, Designs and Issues in Marketing Research.

Populations, Samples, Standard errors, confidence intervals Dr. Omar Al Jadaan.

“Hotspot” algorithm chr5:131,975, ,012,092 Idea: gauge enrichment of tags relative to a local background model based on the number of tags in a 50kb.

Quantitative Testing Plans May 8-10, 2006 Iowa State University, Ames – USA Jean-Louis Laffont Kirk Remund.

Modular 11 Ch 7.1 to 7.2 Part I. Ch 7.1 Uniform and Normal Distribution Recall: Discrete random variable probability distribution For a continued random.

CHAPTER 11 DAY 1. Assumptions for Inference About a Mean  Our data are a simple random sample (SRS) of size n from the population.  Observations from.

Parametric Equations: another process to describe a plane curve on the Cartesian coordinate system – Parametrizations of Plane Curves The coordinates.

Forward-Scan Sonar Tomographic Reconstruction PHD Filter Multiple Target Tracking Bayesian Multiple Target Tracking in Forward Scan Sonar.

Accuracy Based Generation of Thermodynamic Properties for Light Water in RELAP5-3D 2010 IRUG Meeting Cliff Davis.

This material is approved for public release. Distribution is limited by the Software Engineering Institute to attendees. Sponsored by the U.S. Department.

CS654: Digital Image Analysis

The Sampling Design. Sampling Design Selection of Elements –The basic idea of sampling is that by selecting some of the elements in a population, we may.

Lecture PowerPoint Slides Basic Practice of Statistics 7 th Edition.

Data Collection & Sampling Dr. Guerette. Gathering Data Three ways a researcher collects data: Three ways a researcher collects data: By asking questions.

(c) 2000, 2001 SNU CSE Biointelligence Lab Finding Region Another method for processing image  to find “regions” Finding regions  Finding outlines.

Mar 20, 2005 Iterative pe finder: IceTop data compression applied to In-ice D. Seckel, Univ. of Delaware.

Review Normal Distributions –Draw a picture. –Convert to standard normal (if necessary) –Use the binomial tables to look up the value. –In the case of.

1 Outline 1. Why do we need statistics? 2. Descriptive statistics 3. Inferential statistics 4. Measurement scales 5. Frequency distributions 6. Z scores.

POLS 7000X STATISTICS IN POLITICAL SCIENCE CLASS 5 BROOKLYN COLLEGE-CUNY SHANG E. HA Leon-Guerrero and Frankfort-Nachmias, Essentials of Statistics for.

Bayesian Density Regression Author: David B. Dunson and Natesh Pillai Presenter: Ya Xue April 28, 2006.

Robust Watermarking of 3D Mesh Models. Introduction in this paper, it proposes an algorithm that extracts 2D image from the 3D model and embed watermark.

1 Chapter 8 Interval Estimation. 2 Chapter Outline  Population Mean: Known  Population Mean: Unknown  Population Proportion.

A New Class of Mobility Models for Ad Hoc Wireless Networks Rahul Amin Advisor: Dr. Carl Baum Clemson University SURE 2006.

Introduction to emulators Tony O’Hagan University of Sheffield.

Statistical principles: the normal distribution and methods of testing Or, “Explaining the arrangement of things”

Outline Historical note about Bayes’ rule Bayesian updating for probability density functions –Salary offer estimate Coin trials example Reading material:

Zhaoxia Fu, Yan Han Measurement Volume 45, Issue 4, May 2012, Pages 650–655 Reporter: Jing-Siang, Chen.

Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area Cheng Wang and Nancy F. Glenn IEEE GEOSCIENCE AND REMOTE.

Application of Probability Density Function - Optimal Interpolation in Hourly Gauge-Satellite Merged Precipitation Analysis over China Yan Shen, Yang Pan,

Statistical Concepts Breda Munoz RTI International.

Uncertainty Analysis in Emission Inventories

Spatial Data Management

Zhu Han University of Houston Thanks for Professor Dan Wang’s slides

Chapter Six Normal Curves and Sampling Probability Distributions

Chapter 7: Sampling Distributions

Estimating with PROBE II

Towards High Quality Gradient Estimation on Regular Lattices

Statistical Methods For Engineers

12/1/2018 Normal Distributions

Statistics Lecture 12.

CHAPTER 18: Inference about a Population Mean

Nicholas A. Procopio, Ph.D, GISP

Type I and Type II Errors

Presentation transcript:

Density Surfaces Arturas Mazeika

Outline of the Presentation A very brief overview of the architecture of the 3DVDM system An intuition of the density surfaces and main parameters of the method. A case study: sunsite.dk click-stream data The mysterious dataset The theory behind the density surfaces Probability density functions Kernel estimation The density surfaces method The proposals

24/ Arturas Mazeika, WIM3 The Idea of Density Surfaces The idea is to draw a surface, which encloses the data points of a and higher density.

24/ Arturas Mazeika, WIM4 Probability Density Functions PDF reflects the density of the data: higher density means higher PDF value.

24/ Arturas Mazeika, WIM5 Estimation of the PDF Kernel functions are drawn around all observations. All values at point x are added to obtain the overall PDF. K(x)PDF

24/ Arturas Mazeika, WIM6 2D 1D Density Surfaces, 1-2D Cases Surface, which encloses data points of a and higher density is called Density Surface PDF PDF+Cut planeDensity Surface

24/ Arturas Mazeika, WIM7 Density Surfaces, 3D Case DatasetPDF Selecting the brightness level of the PDF yields the Density Surface Different density of the structures complicates the investigation

24/ Arturas Mazeika, WIM8 Implementation of PDF and DS PDF is implemented as an height-unbalanced tree –We start the PDF estimation at g uniformly distributed points –the values of the PDF in between grid points are interpolated linearly –In the region of high oscillation of the PDF more grid points are added to describe the PDF

24/ Arturas Mazeika, WIM9 Key Parameters of the Estimation g is used to detect structures (peaks of the PDF). g has to be as small as possible, but enough to describe the peaks of the PDF g=10 is suitable in most of practical applications The split criteria identifies the range of PDF where grid inadequately describes the PDF. For each 3 neighbor grid points g L, g M, and g R we calculate the difference: if the difference is larger then e then we split the interval. Essentially, the method depends on three parameters: Estimation error e, initial number of grid points g, and split criteria

24/ Arturas Mazeika, WIM10 The Proposals Triangular Surface Reconstruction From a Frame of Points –Envelop the frame of points with a DS –Adjust the algorithm for the tree data structure –Real-Time considerations Separation of Structures –Non-overlapping structures –Overlapping structures Visualization of Fly Through Scenarios –A simple navigational path (a curve + view directions) –(Spiral) curve on a surface –A curve connecting dense regions (visiting hot-spots)