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Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

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The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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Basic Discrete Time Signals01:16

Basic Discrete Time Signals

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The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is...
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Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

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The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
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Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
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Downsampling01:20

Downsampling

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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Related Experiment Video

Updated: Oct 18, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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Entropy Estimation Using a Linguistic Zipf-Mandelbrot-Li Model for Natural Sequences.

Andrew D Back1, Janet Wiles1

  • 1School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia.

Entropy (Basel, Switzerland)
|September 28, 2021
PubMed
Summary

We developed a new Zipf-Mandelbrot-Li model for rapid entropy estimation in natural language sequences. This linguistic constraint-based approach improves accuracy, especially with limited data.

Keywords:
Zipf–Mandelbrot–Li lawentropy estimationlanguage modelsprobabilistic natural sequences

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Area of Science:

  • Computational linguistics
  • Information theory
  • Statistical modeling

Background:

  • Entropy estimation is crucial for analyzing natural sequences but faces challenges with limited data.
  • Existing methods often overlook the unique probability distributions inherent in human and synthetic languages.

Purpose of the Study:

  • To propose a novel, rapid entropy estimation method for natural sequence data.
  • To introduce a new rank-based analytic Zipf-Mandelbrot-Li probabilistic model incorporating linguistic constraints.

Main Methods:

  • Developed a novel analytic Zipfian model with linguistic constraints.
  • Integrated this model into a non-equiprobable coincidence counting algorithm.
  • Applied the method to natural and synthetic language sequence data.

Main Results:

  • The proposed Zipf-Mandelbrot-Li (ZML) model provides more accurate probability distributions for natural sequences.
  • The new entropy estimation method demonstrates effectiveness with limited data.
  • The ZML model shows strong performance in entropy rate estimation tasks.

Conclusions:

  • The linguistic constraint-based ZML model offers a significant advancement in entropy estimation for natural sequences.
  • This approach enhances accuracy and efficiency, particularly in data-scarce scenarios.
  • The method is effective for analyzing emergent languages and other natural sequences.