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It models burst noise (also called popcorn noise or random telegraph signal). If the two possible values that a random variable can take are c 1 {\displaystyle c_{1}} and c 2 {\displaystyle c_{2}} , then the process can be described by the following master equations :
It is also called random telegraph noise (RTN), popcorn noise, impulse noise, bi-stable noise, or random telegraph signal (RTS) noise. It consists of sudden step-like transitions between two or more discrete voltage or current levels, as high as several hundred microvolts , at random and unpredictable times.
For example, processes in the AR(1) model with | | are not stationary because the root of = lies within the unit circle. [3] The augmented Dickey–Fuller test assesses the stability of IMF and trend components. For stationary time series, the ARMA model is used, while for non-stationary series, LSTM models are used to derive abstract features.
Shogun is a free, open-source machine learning software library written in C++. It offers numerous algorithms and data structures for machine learning problems. It offers interfaces for Octave, Python, R, Java, Lua, Ruby and C# using SWIG. It is licensed under the terms of the GNU General Public License version 3 or later.
For an AR(1) process with a positive , only the previous term in the process and the noise term contribute to the output. If φ {\displaystyle \varphi } is close to 0, then the process still looks like white noise, but as φ {\displaystyle \varphi } approaches 1, the output gets a larger contribution from the previous term relative to the noise.
In the C Standard Library, signal processing defines how a program handles various signals while it executes. A signal can report some exceptional behavior within the program (such as division by zero ), or a signal can report some asynchronous event outside the program (such as someone striking an interactive attention key on a keyboard).
Conditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured prediction. Whereas a classifier predicts a label for a single sample without considering "neighbouring" samples, a CRF can take context into account.
A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic or procedure. The algorithm typically uses uniformly random bits as an auxiliary input to guide its behavior, in the hope of achieving good performance in the "average case" over all possible choices of random determined by the random bits; thus either the running time, or the output (or both) are ...