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ksdensity matlab function  (MathWorks Inc)


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    Structured Review

    MathWorks Inc ksdensity matlab function
    “Scrambled” response time and articulatory variability (A and B) Mean (A) and standard deviation (B) of response time (RT) distributions for all subjects and separately for sEEG implants only, and SDE only. Each data point represents the mean or standard deviation of RT for a single subject. Box-and-whisker plots indicate median, upper and lower quartiles, and extent of outliers. Half-violin plots depict probability density estimates for each dataset using default parameters of the <t>ksdensity()</t> <t>MATLAB</t> function. (C) Spectrogram and corresponding phoneme boundaries identified by the Montreal Forced Aligner (McAuliffe et al., 2017) for one “scrambled” production. Time-zero is word onset as manually labeled by inspecting the spectrogram. (D) Probability distribution function of phoneme offsets color-coded from phonemes in (C). These distributions were generated from a subset of subjects ( n = 40) with sufficiently clear audio for automated phonemic transcription.
    Ksdensity Matlab Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/matlab+function+ksdensity/pmc11729016-64-14-15
    Average 90 stars, based on 1 article reviews
    ksdensity matlab function - by Bioz Stars, 2026-10
    90/100 stars

    Images

    1) Product Images from "Supplementary motor area in speech initiation: A large-scale intracranial EEG evaluation of stereotyped word articulation"

    Article Title: Supplementary motor area in speech initiation: A large-scale intracranial EEG evaluation of stereotyped word articulation

    Journal: iScience

    doi: 10.1016/j.isci.2024.111531

    “Scrambled” response time and articulatory variability (A and B) Mean (A) and standard deviation (B) of response time (RT) distributions for all subjects and separately for sEEG implants only, and SDE only. Each data point represents the mean or standard deviation of RT for a single subject. Box-and-whisker plots indicate median, upper and lower quartiles, and extent of outliers. Half-violin plots depict probability density estimates for each dataset using default parameters of the ksdensity() MATLAB function. (C) Spectrogram and corresponding phoneme boundaries identified by the Montreal Forced Aligner (McAuliffe et al., 2017) for one “scrambled” production. Time-zero is word onset as manually labeled by inspecting the spectrogram. (D) Probability distribution function of phoneme offsets color-coded from phonemes in (C). These distributions were generated from a subset of subjects ( n = 40) with sufficiently clear audio for automated phonemic transcription.
    Figure Legend Snippet: “Scrambled” response time and articulatory variability (A and B) Mean (A) and standard deviation (B) of response time (RT) distributions for all subjects and separately for sEEG implants only, and SDE only. Each data point represents the mean or standard deviation of RT for a single subject. Box-and-whisker plots indicate median, upper and lower quartiles, and extent of outliers. Half-violin plots depict probability density estimates for each dataset using default parameters of the ksdensity() MATLAB function. (C) Spectrogram and corresponding phoneme boundaries identified by the Montreal Forced Aligner (McAuliffe et al., 2017) for one “scrambled” production. Time-zero is word onset as manually labeled by inspecting the spectrogram. (D) Probability distribution function of phoneme offsets color-coded from phonemes in (C). These distributions were generated from a subset of subjects ( n = 40) with sufficiently clear audio for automated phonemic transcription.

    Techniques Used: Standard Deviation, Whisker Assay, Labeling, Generated

    Related Articles

    Standard Deviation:

    Article Title: Epithelial apoptotic pattern emerges from global and local regulation by cell apical area.
    Article Snippet: Finally, the probability density estimate of elapsed times between neighboring apoptosis was calculated using the kernel smoothing ksdensity() MATLAB function with a restriction to positive values.

    Article Title: Preventing stress singularities in peri-implant bone - a finite element analysis using a graded bone model.
    Article Snippet: In this study the Matlab function ksdensity was incorporated in the automation script to return a probability density estimate with an automatically chosen bandwidth of 0.2189.

    Article Title: East Asian synoptic climatology linked to Atlantic multidecadal variability.
    Article Snippet: The function was estimated using the MATLAB ksdensity function, which was used to estimate 100 points for univariate data and 900 points for bivariate data at equally spaced points (https://www.mathworks.com/help/stats/ksdensity.html).

    Article Title: Tracking the Cognitive Band in an Open-Ended Task.
    Article Snippet: 4 Using the Matlab function ksdensity.

    Whisker Assay:

    Article Title: Epithelial apoptotic pattern emerges from global and local regulation by cell apical area.
    Article Snippet: Finally, the probability density estimate of elapsed times between neighboring apoptosis was calculated using the kernel smoothing ksdensity() MATLAB function with a restriction to positive values.

    Article Title: Preventing stress singularities in peri-implant bone - a finite element analysis using a graded bone model.
    Article Snippet: In this study the Matlab function ksdensity was incorporated in the automation script to return a probability density estimate with an automatically chosen bandwidth of 0.2189.

    Article Title: East Asian synoptic climatology linked to Atlantic multidecadal variability.
    Article Snippet: The function was estimated using the MATLAB ksdensity function, which was used to estimate 100 points for univariate data and 900 points for bivariate data at equally spaced points (https://www.mathworks.com/help/stats/ksdensity.html).

    Article Title: Tracking the Cognitive Band in an Open-Ended Task.
    Article Snippet: 4 Using the Matlab function ksdensity.

    Labeling:

    Article Title: Epithelial apoptotic pattern emerges from global and local regulation by cell apical area.
    Article Snippet: Finally, the probability density estimate of elapsed times between neighboring apoptosis was calculated using the kernel smoothing ksdensity() MATLAB function with a restriction to positive values.

    Article Title: Preventing stress singularities in peri-implant bone - a finite element analysis using a graded bone model.
    Article Snippet: In this study the Matlab function ksdensity was incorporated in the automation script to return a probability density estimate with an automatically chosen bandwidth of 0.2189.

    Article Title: East Asian synoptic climatology linked to Atlantic multidecadal variability.
    Article Snippet: The function was estimated using the MATLAB ksdensity function, which was used to estimate 100 points for univariate data and 900 points for bivariate data at equally spaced points (https://www.mathworks.com/help/stats/ksdensity.html).

    Article Title: Tracking the Cognitive Band in an Open-Ended Task.
    Article Snippet: 4 Using the Matlab function ksdensity.

    Generated:

    Article Title: Epithelial apoptotic pattern emerges from global and local regulation by cell apical area.
    Article Snippet: Finally, the probability density estimate of elapsed times between neighboring apoptosis was calculated using the kernel smoothing ksdensity() MATLAB function with a restriction to positive values.

    Article Title: Preventing stress singularities in peri-implant bone - a finite element analysis using a graded bone model.
    Article Snippet: In this study the Matlab function ksdensity was incorporated in the automation script to return a probability density estimate with an automatically chosen bandwidth of 0.2189.

    Article Title: East Asian synoptic climatology linked to Atlantic multidecadal variability.
    Article Snippet: The function was estimated using the MATLAB ksdensity function, which was used to estimate 100 points for univariate data and 900 points for bivariate data at equally spaced points (https://www.mathworks.com/help/stats/ksdensity.html).

    Article Title: Tracking the Cognitive Band in an Open-Ended Task.
    Article Snippet: 4 Using the Matlab function ksdensity.



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    Image Search Results


    “Scrambled” response time and articulatory variability (A and B) Mean (A) and standard deviation (B) of response time (RT) distributions for all subjects and separately for sEEG implants only, and SDE only. Each data point represents the mean or standard deviation of RT for a single subject. Box-and-whisker plots indicate median, upper and lower quartiles, and extent of outliers. Half-violin plots depict probability density estimates for each dataset using default parameters of the ksdensity() MATLAB function. (C) Spectrogram and corresponding phoneme boundaries identified by the Montreal Forced Aligner (McAuliffe et al., 2017) for one “scrambled” production. Time-zero is word onset as manually labeled by inspecting the spectrogram. (D) Probability distribution function of phoneme offsets color-coded from phonemes in (C). These distributions were generated from a subset of subjects ( n = 40) with sufficiently clear audio for automated phonemic transcription.

    Journal: iScience

    Article Title: Supplementary motor area in speech initiation: A large-scale intracranial EEG evaluation of stereotyped word articulation

    doi: 10.1016/j.isci.2024.111531

    Figure Lengend Snippet: “Scrambled” response time and articulatory variability (A and B) Mean (A) and standard deviation (B) of response time (RT) distributions for all subjects and separately for sEEG implants only, and SDE only. Each data point represents the mean or standard deviation of RT for a single subject. Box-and-whisker plots indicate median, upper and lower quartiles, and extent of outliers. Half-violin plots depict probability density estimates for each dataset using default parameters of the ksdensity() MATLAB function. (C) Spectrogram and corresponding phoneme boundaries identified by the Montreal Forced Aligner (McAuliffe et al., 2017) for one “scrambled” production. Time-zero is word onset as manually labeled by inspecting the spectrogram. (D) Probability distribution function of phoneme offsets color-coded from phonemes in (C). These distributions were generated from a subset of subjects ( n = 40) with sufficiently clear audio for automated phonemic transcription.

    Article Snippet: Half-violin plots depict probability density estimates for each dataset using default parameters of the ksdensity() MATLAB function. (C) Spectrogram and corresponding phoneme boundaries identified by the Montreal Forced Aligner (McAuliffe et al., 2017) for one “scrambled” production.

    Techniques: Standard Deviation, Whisker Assay, Labeling, Generated