Probability distribution
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Back to Top2013 13th Canadian Workshop on Information Theory (CWIT)
The 13th Canadian Workshop on Information Theory will take place in Toronto, Ontario, Canada from Tuesday, May 28, to Friday, May 31, 2013. Previously unpublished contributions from a broad range of topics in information theory and its applications are solicited, including (but not limited to) the following areas: Coded modulation, Coding theory and practice, Communication complexity, Communication systems, Cryptology and data security, Data compression, Detection and estimation, Information theory and statistics, Network coding, Interactive information theory, Pattern recognition and learning, Quantum information processing, Shannon theory, Signal processing, Cognitive radio, Cooperative communication, Multiterminal information theory, and Information theory in biology. International researchers are welcome to attend and present research at this event.
2013 13th International Conference on Control, Automaton and Systems (ICCAS)
Control Theory and Application, Intelligent Systems, Industrial Applications of Control,Sensor and Signal Processing, Control Devices and Instruments, Robot Control, RobotVision, HumanRobot Interaction, Robotic Applications, Unmanned Vehicle Systems...
2013 International Conference on Machine Learning and Cybernetics (ICMLC)
Statistical Machine Learning, Intelligent & fuzzy control, Pattern Recognition , Ensemble method, Evolutionary computation, Fuzzy & rough set, Data & web mining , Intelligent Business Computing , Biometrics , Bioinformatics , Information retrieval, Cybersecurity, Web intelligence and technology, Semantics & ontology engineering, Social Networks & Ubiquitous Intelligence, Multicriteria decision making, Soft Computing, Intelligent Systems, Speech, Image & Video Processing, Decision Support System
2012 IEEE International Conference on Automation and Logistics (ICAL)
Automation, logistics, and related areas.
2011 9th IEEE International Conference on Control and Automation (ICCA)
IEEE ICCA 2001 aims to create a forum for scientists and practicing engineers throughout the world to present the latest research findings and ideas in the areas of control and automation.
Periodicals related to Probability distribution
Back to TopIndustrial Electronics, IEEE Transactions on
Theory and applications of industrial electronics and control instrumentation science and engineering, including microprocessor control systems, highpower controls, process control, programmable controllers, numerical and program control systems, flow meters, and identification systems.
Information Theory, IEEE Transactions on
The fundamental nature of the communication process; storage, transmission and utilization of information; coding and decoding of digital and analog communication transmissions; study of random interference and informationbearing signals; and the development of informationtheoretic techniques in diverse areas, including data communication and recording systems, communication networks, cryptography, detection systems, pattern recognition, learning, and automata.
Medical Imaging, IEEE Transactions on
Imaging methods applied to living organisms with emphasis on innovative approaches that use emerging technologies supported by rigorous physical and mathematical analysis and quantitative evaluation of performance.
Power Systems, IEEE Transactions on
Requirements, planning, analysis, reliability, operation, and economics of electrical generating, transmission, and distribution systems for industrial, commercial, public, and domestic consumption.
Signal Processing Letters, IEEE
Rapid dissemination of new results in signal processing worldwide.
Xplore Articles related to Probability distribution
Back to TopDesign of Pattern Classifiers with the Updating Property Using Stochastic Approximation Techniques
Computers, IEEE Transactions on, 1968
AbstractA nonparametric training procedure for finding the optimal weights of the discriminant functions of a pattern classifier in any optimization criterion, expressible as a convex function from an arbitrary sequence of sample patterns, is proposed. This design procedure is based on the stochastic approximation technique, and has the updating property because it processes the sample patterns whenever they become available. ...
Automated seeded lesion segmentation on digital mammograms
Medical Imaging, IEEE Transactions on, 1998
Segmenting lesions is a vital step in many computerized massdetection schemes for digital (or digitized) mammograms. The authors have developed two novel lesion segmentation techniquesone based on a single feature called the radial gradient index (RGI) and one based on simple probabilistic models to segment mass lesions, or other similar nodular structures, from surrounding background. In both methods a series ...
An Experimental Analysis of Suggestions in Collaborative Tagging
Web Intelligence and Intelligent Agent Technologies, 2009. WIIAT '09. IEEE/WIC/ACM International Joint Conferences on, 2009
Most tagging systems support the user in the tag selection process by providing tag suggestions, or recommendations, based on a popularity measurement of tags other users provided when tagging the same resource, like a webpage. In this paper we investigate the influence of tag suggestions on the emergence of powerlaw distributions as a result of collaborative tag behavior. Although previous ...
Information bounds for random signals in timefrequency plane
Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on, 2001
Renyi entropy has been proposed as one of the methods for measuring signal information content and complexity on the timefrequency plane. It provides a quantitative measure for the uncertainty of the signal. All of the previous work concerning Renyi entropy in the timefrequency plane has focused on determining the number of signal components in a given deterministic signal. We discuss ...
Discrete Basis and Computation of Elementary Functions
Computers, IEEE Transactions on, 1985
We give necessary and sufficient conditions in order that the infinite product or sum of the terms of a positive decreasing sequence generates the reals in a given interval.
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Educational Resources on Probability distribution
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Low Voltage Low Frequency Continuous Time CMOS Antialiasing Filters
W. Machowski; J. Jasielski; S. Kuta 2007 14th International Conference on Mixed Design of Integrated Circuits and Systems, 2007
In the paper design considerations for low frequency antialiasing filters suitable for low voltage modern CMOS technology. Two circuit implementations of SallenKey architecture are presented. First uses conventional approach and utilizes long tail pair as amplifying block, while the second exploits CMOS inverters for the same purpose and thus its supply requirements are lower. In both a tricky solution of ...
Likelihood Ratio Derivative Estimators For Stochastic Systems
P. W. Glynn Simulation Conference Proceedings, 1989. Winter, 1989
First Page of the Article ![](/xploreAssets/images/absImages/00718702.png)
Regression Databases: Probabilistic Querying Using Sparse Learning Sets
Alexander Brodsky; Carlotta Domeniconi; David Etter 2006 5th International Conference on Machine Learning and Applications (ICMLA'06), 2006
We introduce regression databases (REDB) to formalize and automate probabilistic querying using sparse learning sets. The REDB data model involves observation data, learning set data, views definitions, and a regression model instance. The observation data is a collection of relational tuples over a set of attributes; the learning data set involves a subset of observation tuples, augmented with learned attributes, ...
Minghu Jiang; G. Gielen; Beixong Deng; Xiaofang Tang; Qiuqi Ruan; Baozong Yuan Signal Processing, 2002 6th International Conference on, 2002
A statistical model of quantization was used to analyze the effects of quantization in digital implementation, and the performance degradation caused by number of quantized bits in multilayer feedforward neural networks (MLFNN) of different probability distribution. The performance of the training was compared with and without clipping weights for MLFNN. We established and analyzed the relationships between inputs and outputs ...
NonGaussian optical field statistics in a longhaul solitonbased DPSK transmission system
G. M. Donovan; W. L. Kath; E. T. Spiller 2005 Quantum Electronics and Laser Science Conference, 2005
We demonstrate with importancesampled MonteCarlo simulations that the tails of the optical field's probability distribution at the end of a longhaul solitonbased DPSK transmission system are strongly nonGaussian.
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Appendix E: Three Probability Distribution Tables
Five appendices provide the background knowledge needed in power system risk assessment, including: Basic probability concepts Elements of Monte Carlo Simulation AC and DC power flow models Optimization algorithms used in the risk evaluation model of power systems Three probability distribution tables used in parameter estimation of outage models

Random Variables and Probability Distributions
This chapter contains sections titled: Definitions Probability Distribution Functions Discrete Random Variables Continuous Random Variables Statistically Independent Random Variables Functions of Random Variables Random Processes Problems

This chapter contains sections titled: Electronics Review The Probability Distribution of Electronemission Times Average Current through a Temperaturelimited Diode Shotnoise Spectral Density for a Temperaturelimited Diode Shotnoise Probability Density for a Temperaturelimited Diode Spacecharge Limiting of Diode Current Shot Noise in a Spacechargelimited Diode Shot Noise in Spacechargelimited Triodes and Pentodes Problems

Fast Weighted Exponential Product Rules for Robust General MultiRobot Data Fusion
This paper considers the distributed data fusion (DDF) problem for general multiagent robotic sensor networks in applications such as 3D mapping and target search. In particular, this paper focuses on the use of conservative fusion via the weighted exponential product (WEP) rule to combat inconsistencies that arise from doublecounting common information between fusion agents. WEP fusion is ideal for fusing arbitrarily distributed estimates in adhoc communication network topologies, but current WEP rule variants have limited applicability to general multirobot DDF. To address these issues, new informationtheoretic WEP metrics are presented along with novel optimization algorithms for efficiently performing DDF within a recursive Bayesian estimation framework. While the proposed WEP fusion methods are generalizable to arbitrary probability distribution functions (pdfs), emphasis is placed here on widelyused Bernoulli and Gaussian mixture pdfs. Experimental results for multirobot 3D mapping and target search applications show the effectiveness of the proposed methods.

Distributed Approximation of Joint Measurement Distributions Using Mixtures of Gaussians
This paper presents algorithms to distributively approximate the continuous probability distribution that describes the fusion of sensor measurements from many networked robots. Each robot forms a weighted mixture of scaled Gaussians to represent the continuous measurement distribution (i.e., likelihood) of its local observation. From this mixture set, each robot then draws samples of Gaussian elements to enable the use of a consensusbased algorithm that evolves the corresponding canonical parameters. We show that these evolved parameters form a distribution that converges weakly to the joint of all the robots' unweighted mixture distributions, which itself converges weakly to the joint measurement distribution as more system resources are allocated. The innovation of this work is the combination of samplebased sensor fusion with the notion of preconvergence termination without the risk of 'double counting' any single observation. We also derive bounds and convergence rates for the approximated joint measurement distribution, specifically the elements of its information vectors and the eigenvalues of its information matrices. Most importantly, these performance guarantees do not come at a significant cost of complexity, since computational and communication complexity of the canonical parameters scales quadratically with respect to the Gaussian dimension, linearly with respect to the number of samples, and constant with respect to the number of robots. Results from numerical simulations for object localization are discussed using both Gaussians and mixtures of Gaussians.

FeatureBased Prediction of Trajectories for Socially Compliant Navigation
Mobile robots that operate in a shared environment with humans need the ability to predict the movements of people to better plan their navigation actions. In this paper, we present a novel approach to predict the movements of pedestrians. Our method reasons about entire trajectories that arise from interactions between people in navigation tasks. It applies a maximum entropy learning method based on features that capture relevant aspects of the trajectories to determine the probability distribution that underlies human navigation behavior. Hence, our approach can be used by mobile robots to predict forthcoming interactions with pedestrians and thus react in a socially compliant way. In extensive experiments, we evaluate the capability and accuracy of our approach and demonstrate that our algorithm outperforms the popular social forces method, a stateoftheart approach. Furthermore, we show how our algorithm can be used for autonomous robot navigation using a real robot.

On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts
One of the intuitions underlying many graphbased methods for clustering and semisupervised learning, is that class or cluster boundaries pass through areas of low probability density. In this paper we provide some formal analysis of that notion for a probability distribution. We introduce a notion of weighted boundary volume, which measures the length of the class/cluster boundary weighted by the density of the underlying probability distribution. We show that sizes of the cuts of certain commonly used data adjacency graphs converge to this continuous weighted volume of the boundary.

Fundamentals of Probability and Statistics
This chapter contains sections titled: Concept of Frequency Important Parameters of Frequency Distribution Theory of Probability Probability Distribution Model Sampling Theory Statistical Decision Making Conclusions References

In these notes, we introduce particle filtering as a recursive importance sampling method that approximates the minimummeansquareerror (MMSE) estimate of a sequence of hidden state vectors in scenarios where the joint probability distribution of the states and the observations is nonGaussian and, therefore, closedform analytical expressions for the MMSE estimate are generally unavailable. We begin the notes with a review of Bayesian approaches to static (i.e., timeinvariant) parameter estimation. In the sequel, we describe the solution to the problem of sequential state estimation in linear, Gaussian dynamic models, which corresponds to the wellknown Kalman (or KalmanBucy) filter. Finally, we move to the general nonlinear, nonGaussian stochastic filtering problem and present particle filtering as a sequential Monte Carlo approach to solve that problem in a statistically optimal way. We review several techniques to improve the performance of particle filters, including importa ce function optimization, particle resampling, Markov Chain Monte Carlo move steps, auxiliary particle filtering, and regularized particle filtering. We also discuss RaoBlackwellized particle filtering as a technique that is particularly wellsuited for many relevant applications such as fault detection and inertial navigation. Finally, we conclude the notes with a discussion on the emerging topic of distributed particle filtering using multiple processors located at remote nodes in a sensor network. Throughout the notes, we often assume a more general framework than in most introductory textbooks by allowing either the observation model or the hidden state dynamic model to include unknown parameters. In a fully Bayesian fashion, we treat those unknown parameters also as random variables. Using suitable dynamic conjugate priors, that approach can be applied then to perform joint state and parameter estimation. Table of Contents: Introduction / Bayesian Estimation of Static Vectors / he Stochastic Filtering Problem / Sequential Monte Carlo Methods / Sampling/Importance Resampling (SIR) Filter / Importance Function Selection / Markov Chain Monte Carlo Move Step / RaoBlackwellized Particle Filters / Auxiliary Particle Filter / Regularized Particle Filters / Cooperative Filtering with Multiple Observers / Application Examples / Summary

Distribution of the Phase Angle Between Two Vectors Perturbed by Gaussian Noise
The probability distribution of the phase angle between two vectors perturbed by correlated Gaussian noises is studied in detail. Definite integral expressions are derived for the distribution function, and its asymptotic behavior for large signaltonoise is found for small, near /2, and large angles. The results are applied to obtain new formulas for the symbol error rate in MDPSK, to calculate the distribution of instantaneous frequency, to study the error rate in digital FM with partialbit integration in the post detection filter, and to obtain a simplified expresion for the error rate in DPSK with a phase error in the reference signal. In the degenerate case in which one of the vectors is noise free, the results lead to the symbol error rate in MPSK.
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