Probability density function

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In probability theory, a probability density function (pdf), or density of a continuous random variable is a function that describes the relative likelihood for this random variable to occur at a given point. (

Conferences related to Probability density function

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2020 IEEE International Conference on Plasma Science (ICOPS)

IEEE International Conference on Plasma Science (ICOPS) is an annual conference coordinated by the Plasma Science and Application Committee (PSAC) of the IEEE Nuclear & Plasma Sciences Society.

2019 IEEE 46th Photovoltaic Specialists Conference (PVSC)

Photovoltaic materials, devices, systems and related science and technology

2019 IEEE 58th Conference on Decision and Control (CDC)

The CDC is recognized as the premier scientific and engineering conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, systems and control, and related areas.The 58th CDC will feature contributed and invited papers, as well as workshops and may include tutorial sessions.The IEEE CDC is hosted by the IEEE Control Systems Society (CSS) in cooperation with the Society for Industrial and Applied Mathematics (SIAM), the Institute for Operations Research and the Management Sciences (INFORMS), the Japanese Society for Instrument and Control Engineers (SICE), and the European Union Control Association (EUCA).

2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

International Geosicence and Remote Sensing Symposium (IGARSS) is the annual conference sponsored by the IEEE Geoscience and Remote Sensing Society (IEEE GRSS), which is also the flagship event of the society. The topics of IGARSS cover a wide variety of the research on the theory, techniques, and applications of remote sensing in geoscience, which includes: the fundamentals of the interactions electromagnetic waves with environment and target to be observed; the techniques and implementation of remote sensing for imaging and sounding; the analysis, processing and information technology of remote sensing data; the applications of remote sensing in different aspects of earth science; the missions and projects of earth observation satellites and airborne and ground based campaigns. The theme of IGARSS 2019 is “Enviroment and Disasters”, and some emphases will be given on related special topics.

2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting

The conference is intended to provide an international forum for the exchange of information on state-of-the-art research in antennas, propagation, electromagnetics, and radio science.

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Periodicals related to Probability density function

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.

Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.

Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...

Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...

Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.

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Most published Xplore authors for Probability density function

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Xplore Articles related to Probability density function

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On Finite Block-Length Quantization Distortion

[{u'author_order': 1, u'full_name': u'Chen Gong'}, {u'author_order': 2, u'full_name': u'Xiaodong Wang'}] IEEE Transactions on Information Theory, None

We investigate the upper and lower bounds on the quantization distortions for independent and identically distributed continuous sources in the finite block-length regime. We derive a lower bound on the quantization distortion, as well as an upper bound on the quantization distortion based on random quantization codebooks. Moreover, we apply the obtained bounds to the continuous Gaussian source. For the ...

Keynote III (joint with PPoPP)

[] 2009 IEEE 15th International Symposium on High Performance Computer Architecture, 2009


Statistical Analysis of Partial Discharge Data Based on Master Equation

[{u'author_order': 1, u'full_name': u'R. Liao'}, {u'author_order': 2, u'full_name': u'G. A. Taylor'}, {u'author_order': 3, u'full_name': u'M. R. Irving'}] 2011 46th International Universities' Power Engineering Conference (UPEC), 2011

In this paper, we apply stochastic master equation (ME) to study the dynamic of probability density function (PDF) in the state space spanned by the PD peak variable and load variable describing the states of the Partial Discharge (PD) process. The states of the PD process are obtained by dividing the state space into several regions. Through studying the frequencies ...

Data Transmission and Integrity

[{u'author_order': 1, u'full_name': u'John W. Leis'}] Communication Systems Principles Using MATLAB, None

This chapter distinguishes between error detection and error correction. It shows calculation of the bit error probabilities for a simple channel coding scheme. The chapter helps the reader to understand the working of algorithms for block error detection and block error correction. It explains the operation of convolutional coding, including path‐search algorithms. The chapter discusses private key encryption, key‐exchange methods, ...

Session TA1b: Compressive sensing

[{u'author_order': 1, u'full_name': u'Emmanuel Candes'}] 2008 42nd Asilomar Conference on Signals, Systems and Computers, 2008


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Educational Resources on Probability density function

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No eLearning Articles are currently tagged "Probability density function" Videos

Designing Reconfigurable Large-Scale Deep Learning Systems Using Stochastic Computing - Ao Ren: 2016 International Conference on Rebooting Computing
AlGaN/GaN Plasmonic Terahertz Detectors
Micro-Apps 2013: Understanding Probability of Intercept for Intermittent Signals
EMBC 2011-Keynote-Kamil Ugurbil-Frontiers in Neuroimaging: from Cortical Columns to Whole Brain Function, Connectivity and Morphology
Advances in MgB2 - ASC-2014 Plenary series - 7 of 13 - Wednesday 2014/8/13
International Future Energy Challenge (IFEC) 2017
Materials Challenges for Next-Generation, High-Density Magnetic Recording - Kazuhiro Hono: IEEE Magnetics Distinguished Lecture 2016
International Future Energy Challenge 2018
Octopus-Inspired Robot Can Grasp, Crawl and Swim -- IEEE Spectrum Report
IMS 2015: Wearable electronics - why wear it?
Infineon Technologies: Power Efficiency from Generation to Consumption
KeyTalks: The US Department of Energy's Perspective on Achieving Low Cost High Efficiency Energy
How Symmetry Constrains Evolutionary Optimizers: A Black Box Differential Evolution Case Study - IEEE Congress on Evolutionary Computation 2017
Multi-Function VCO Chip for Materials Sensing and More - Jens Reinstaedt - RFIC Showcase 2018
Ted Berger: Far Futures Panel - Technologies for Increasing Human Memory - TTM 2018
Bari-Bari-II: Jack-Up Rescue Robot with Debris Opening Function
Playing Games with Computational Intelligence
Ponnuthurai Nagaratnam Suganthan - Differential Evolution
Next Generation Power Supplies - APEC 2016
IEEE Themes - Distance-Dependent Kronecker Graphs For Modeling Social Networks


  • Data Transmission and Integrity

    This chapter distinguishes between error detection and error correction. It shows calculation of the bit error probabilities for a simple channel coding scheme. The chapter helps the reader to understand the working of algorithms for block error detection and block error correction. It explains the operation of convolutional coding, including path‐search algorithms. The chapter discusses private key encryption, key‐exchange methods, and public‐key encryption. It reviews two useful concepts in modeling data transmission integrity. These are probability concepts and integer arithmetic. Calculations for data transfer integrity checking, and newer approaches to data security and encryption, depend on integer arithmetic in general and modulo arithmetic in particular. The chapter introduces the key concept of the bit error rates and relates it to the system overall, the transmitted signal power, and the external noise encountered.

  • DBRV Method for Calculation of the ISI Statistic

    This chapter contains sections titled:IntroductionMatrix Method for the Total ISISimulations of ISI PDFConcepts for the Gaussian StatisticDBRV Method for the Total ISIConclusionsReferences

  • Quantization and Coding

    This chapter discusses principles of scalar quantization and explains the operation of a vector quantization. It explores the principles of minimum‐redundancy code word assignment and provides the important algorithm classes for lossless. The chapter also explains several image compression approaches, including the Discrete Cosine Transform. It helps the reader to understand the basic approach to waveform and parametric speech encoding and then explains the advantages and disadvantages of each. The chapter also explores the key requirements for audio encoders and the building blocks that go to make up an audio encoding system. It reviews some of the notions of probability, which are useful in modeling errors in communication channels, as well as difference equations, which are used extensively in signal encoding. The chapter gives an overview of coding: Image Coding; Source Coding; and Speech and Audio Coding. It also discusses digital channel capacity.

  • Basics

    This chapter contains sections titled:Noise: Definition, Modeling, PropertiesSignal: Definition, Modeling, PropertiesClassification: Suppression, Cancellation, EnhancementSampling and QuantizationAudio Processing in the Frequency DomainBandwidth LimitingSignal‐to‐Noise‐Ratio: Definition and MeasurementSubjective Quality MeasurementOther Methods for Quality and Enhancement MeasurementSummaryBibliography

  • Statistical Distributions and Random Number Generation


  • Continuous Random Variables

    This chapter provides a brief introduction to the continuous random variables in particular and contrasting this concept with discrete random variables. Several important continuous random variables are applicable to reliability theory. We will see in later chapters how these concepts are applied to more practical reliability problems.

  • Random Signals

    This chapter provides an overview of integration of the Gaussian probability density function and the Q-function. It explains the weighted sum of random variables and properties of Gaussian variables. The chapter presents the central limit theorem and ensembles average, autocorrelation functions of random processes. It also explores statistical properties of additive white Gaussian noise (AWGN). The chapter provides step-by-step code exercises and instructions to implement execution sequences. The MATLAB command randn(1,b) generates a 1×b vector whose elements are realizations of independent and identically distributed Gaussian random variables with zero mean and unit variance. The chapter summarizes the analytical relationship among the input, the output, and the impulse response of a linear system in the time domain. It is designed to help teach and understand communication systems using a classroom-tested, active learning approach.

  • Probability and Random Variables

    This chapter reviews uniform and Gaussian random variables (RVs). It describes the empirical probability density function (PDF) of RVs and provides its comparison with the theoretical PDF. Using MATLAB functions such as random(), rand(), and randn(), the authors generate various kinds of RVs. Although the built-in function histogram() is convenient for generating the empirical distribution, the chapter provides the detailed steps to obtain the distribution to gain an in-depth understating of the PDF concept. The MATLAB function randn, every time it is invoked, generates a sample of the Gaussian RV with zero mean and unit variance. The mean and the variance are calculated using numerical integration. The chapter also discusses Rayleigh fading model, which is one of the commonly encountered fading channel models in wireless communications. The chapter is designed to help teach and understand communication systems using a classroom-tested, active learning approach.

  • A Nonparametric Approach for River Flow Forecasting Based on Autonomous Neural Network Models

    This chapter presents an inductive learning procedure that combines several techniques to generate a fully data‐driven forecasting model. It considers a forecasting method based on appropriate techniques for controlling Artificial neural networks (ANNs) complexity with simultaneous selection of explanatory input variables via a combination of filter and wrapper techniques. Input selection is performed, without user intervention, by applying Chaos theory and Bayesian inference. The challenging problem of rainfall forecasting is employed for showing the robustness of the proposed technique in dealing with different time‐series dynamics. In nonlinear chaotic time‐series analysis, local models are developed via the application of an automatic clustering algorithm based on the rival penalized expectation‐maximization (RPEM) algorithm. Neural network models are estimated, without cross‐validation, relying on data partitioning and Bayesian regularization for complexity control. The proposed forecasting model has been successfully tested using rainfall data from six major hydrographic basins in Brazil.

  • How to Use Statistics to Ensure a Manufacturable Design

    This chapter contains sections titled: * Independent Component Failures * Using the Gaussian Distribution * Setting a Manufacturing Test Limit * Procuring a Custom Component * Problems

Standards related to Probability density function

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IEEE Recommended Practice for Determining the Peak Spatial-Average Specific Absorption Rate (SAR) in the Human Head from Wireless Communications Devices: Measurement Techniques

To specify protocols for the measurement of the peak spatial-average specific absorption rate (SAR) in a simplified model of the head of users of hand-held radio transceivers used for personal wireless communications services and intended to be operated while held next to the ear. It applies to contemporary and future devices with the same or similar operational characteristics as contemporary ...

IEEE Standard for Safety Levels With Respect to Human Exposure to Electromagnetic Fields, 0-3 kHz

Develop safety levels for human exposure to electromagnetic fields from 0 to 3kHz. This standard will be based on the results of an evaluation of the relevant scientific literature and proven effects which are well established and for which thresholds of reaction are understood. Field limits will be derived from threshold current densities or internal electric fields.

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