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matlab function ksdensity  (MathWorks Inc)


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    MathWorks Inc matlab function ksdensity
    Matlab Function Ksdensity, 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/pmc11999189-80-21-19
    Average 90 stars, based on 1 article reviews
    matlab function ksdensity - by Bioz Stars, 2026-10
    90/100 stars

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    Article Title: The Statistical Structure of the Hippocampal Code for Space as a Function of Time, Context, and Value.
    Article Snippet: DF/F0 was calculated as (F F0) / F0, where F is the time series of raw fluorescence averaged over the pixels in an ROI, F0 is the mode of a kernel density estimate (MATLAB function ‘‘ksdensity’’) of F within a run (calculation was done for each run).

    Article Title: Voluntary alcohol consumption disrupts coupling of prefrontal cortical activity to arousal
    Article Snippet: The DFF (ΔF/F) for each neuron was calculated as ΔF/F(t) = 100 x (F(t) – F 0 )/F 0 , where F 0 represents the mode of the distribution of fluorescence values (estimated using the MATLAB function ‘ksdensity’).

    Article Title: Hydrodynamics and direction change of tumbling bacteria
    Article Snippet: We then obtain the resulting probability density function (pdfs) for γ using the Matlab function ‘ksdensity’ .

    Article Title: Long-Distance Descending Spinal Neurons Ensure Quadrupedal Locomotor Stability.
    Article Snippet: One-dimensional kernel densities were obtained using the MATLAB function ‘‘ksdensity.’’ Two-dimensional kernel density estimation used to compute the distribution contours was obtained using the MATLAB function ‘‘kde2d’’ with the contour lines connecting points of equal densities and drawn for density values between 20% and 100% of the estimated density range, in six steps.

    Article Title: Experimental and finite element robustness studies of a bumper system subjected to an offset impact loading
    Article Snippet: Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform.. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content.. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis.

    Article Title: Advancing our ability to quantify an individual’s habitual motion path and deviation when running
    Article Snippet: Quantification of one’s deviation from their HMP was completed as described below: Defining Kernel density plots for each joint angle Kernel density plots (KDPs) were created from joint angle data from (1) habitual movements and (2) the sock run (Figure 1) using the MATLAB function ‘ksdensity’ with default settings and equally weighted data points.



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    matlab function ksdensity  (MathWorks Inc)
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    ksdensity matlab function  (MathWorks Inc)
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    “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.
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    “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.
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    “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.
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    “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