Generalized functions | Stochastic calculus
In probability theory, a branch of mathematics, white noise analysis, otherwise known as Hida calculus, is a framework for infinite-dimensional and stochastic calculus, based on the Gaussian white noise probability space, to be compared with Malliavin calculus based on the Wiener process. It was initiated by Takeyuki Hida in his 1975 Carleton Mathematical Lecture Notes. The term white noise was first used for signals with a flat spectrum. (Wikipedia).
Jonathan defines what white noise actually is and how it's used to mask other annoying sounds. Learn more at HowStuffWorks.com: http://science.howstuffworks.com/question47.htm Share on Facebook: http://goo.gl/n7YNrZ Share on Twitter: http://goo.gl/Fq9InS Subscribe: http://goo.gl/ZYI7Gt V
From playlist Episodes hosted by Jonathan
Time Series Talk : White Noise
Intro to white noise in time series analysis
From playlist Time Series Analysis
What if there was a way to create white noise for your sense of smell? Trace is here to explain how scientists were able to successfully mask odors using “white smell.” Read More: White smell: the olfactory equivalent of white noise http://www.newscientist.com/article/dn22514-white-sm
From playlist DNews Favorites
Analysis of Quantization Error
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Modeling quantization error as uncorrelated noise. Signal to quantization noise ratio as a function of the number of bits used to represent the sign
From playlist Sampling and Reconstruction of Signals
Discrete Fourier Transform - Simple Step by Step
Easy explanation of the Fourier transform and the Discrete Fourier transform, which takes any signal measured in time and extracts the frequencies in that signal. This is a work in progress, let me know if anything doesn't make sense, and I will update the video to make that clearer. Tha
From playlist Fourier
What is Sound? - Quickly Discover What Sound Really Is
What is Sound? This simple demonstration visually shows how sound waves are produced from a vibrating surface. A frequency generator is hooked up to a power amplifier, and the resultant signal is used to drive a loudspeaker. The signal is also sent to an oscilloscope. After listen
From playlist Physics Demonstrations
Guy Nason: A test for local white noise (and the absence of aliasing) in locally stationary...
Abstract: This talk develops a new test for local white noise which also doubles as a test for the lack of aliasing in a locally stationary wavelet process. We compare and contrast our new test with the aliasing test for stationary time series due to Hinich and co-authors. We show that the
From playlist Probability and Statistics
What is signal and what is noise?
This lecture discusses the distinction between "signal" and "noise" -- and important definition when working with large or complex datasets. This video is part of an online course called "Simulate, understand, & visualize data like a data scientist." The course includes 3+ hours of video
From playlist Simulate, understand, and visualize data
Data Noise | Introduction to Data Mining part 8
In this Data Mining Fundamentals tutorial, we discuss data noise that can overlap valid data and outliers. Noise can appear because of human inconsistency and labeling. We will provide you with several examples of data noise, and how data noise can be measured and recorded. -- Learn more a
From playlist Introduction to Data Mining
Simulating data to understand analysis methods
This video lesson is part of a complete course on neuroscience time series analyses. The full course includes - over 47 hours of video instruction - lots and lots of MATLAB exercises and problem sets - access to a dedicated Q&A forum. You can find out more here: https://www.udemy.
From playlist NEW ANTS #1) Introductions
Welcome to Quantitative Risk Management (QRM). In Lesson 6 we start discussing Time Series (TS) analysis, which we will later combine with EVT. We will answer the following questions: What is a TS? What types of TS can we model? What does stationarity mean? What are the main causes of non
From playlist Quantitative Risk Management
This is the first lecture on "A Markovian perspective on some singular SPDEs" taught by Professor Nicolas Perkowski. For more materials and slides visit: https://sites.google.com/view/oneworld-pderandom/home
From playlist Summer School on PDE & Randomness
Time Series class: Part 1 - Dr Ioannis Papastathopoulos, University of Edinburgh
Part 2: https://youtu.be/7n0HTtThMe0 Introduction: Moving average, Autoregressive and ARMA models. Parameter estimation, likelihood based inference and forecasting with time series. Advanced: State-space models (hidden Markov models, Kalman filter) and applications. Recurrent neural netw
From playlist Data science classes
Neuroscience source separation 1b: Spectral separation in MATLAB
This is part one of a three-part lecture series I taught in a masters-level neuroscience course in fall of 2020 at the Donders Institute (the Netherlands). The lectures were all online in order to minimize the spread of the coronavirus. That's good for you, because now you can watch the en
From playlist Neuroscience source separation (3-part lecture series)
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Representing multivariate random signals using principal components. Principal component analysis identifies the basis vectors that describe the la
From playlist Random Signal Characterization
Michael Unser: Wavelets and stochastic processes: how the Gaussian world became sparse
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist 30 years of wavelets
Spinodal decomposition in the Allen-Cahn equation without and with noise
This simulation compares solutions of the Allen-Cahn equation without and with noise. The left half of the display shows the case without noise, while the right half shows the case with an additional space-time white noise, meaning here that independent Gaussian random variables are added
From playlist Reaction-diffusion equations