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ksdensity function in matlab version r2023a, 9.14.0.2337262  (MathWorks Inc)


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    MathWorks Inc ksdensity function in matlab version r2023a, 9.14.0.2337262
    Ksdensity Function In Matlab Version R2023a, 9.14.0.2337262, 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/pm39577175-173-5-8
    Average 90 stars, based on 1 article reviews
    ksdensity function in matlab version r2023a, 9.14.0.2337262 - by Bioz Stars, 2026-09
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    Article Title: The dynamics of Cryptococcus neoformans infection in Galleria mellonella .
    Article Snippet: For kernel smoothing, we applied MATLAB's built-in ksdensity function to the sample histogram.

    Article Title: Spike reliability is cell type specific and shapes excitation and inhibition in the cortex
    Article Snippet: Using the obtained distribution, we computed the relative cumulative density function using the ksdensity function from Matlab (function parameter: cdf).

    Article Title: Reliability and accuracy of single-molecule FRET studies for characterization of structural dynamics and distances in proteins
    Article Snippet: We also performed a kernel density estimation of the interdye distance distribution without explicitly accounting for linker broadening using a Gaussian kernel by the ksdensity function of MATLAB, which computes the theoretically optimal bandwidth for normally distributed data49.

    Article Title: Behavior classification: Introducing machine learning approaches for classification of sign-tracking, goal-tracking and beyond.
    Article Snippet: Using the ksdensity function from MATLAB, we obtained an estimate of the density probability of the scores.

    Article Title: Locating activation sites of TMS with opposite current directions using probabilistic modelling and biophysical axon models.
    Article Snippet: To visualize the posterior distributions, we used the ksdensity function of MATLAB to calculate 1D and 2D kernel density estimates of the posterior probability.

    Article Title: Breath Analysis of Propofol and Associated Metabolic Signatures: A Pilot Study Using Secondary Electrospray Ionization High-Resolution Mass Spectrometry
    Article Snippet: 284 words; Introduction: 378 words; Discussion: 1491 words; Number of Figures: 4; Number of Tables: 1; Number of Supplementary Digital Files: 8.. Abbreviated Title: Breath-Based Propofol Pharmacometabolomics.. Summary Statement: Not applicable.

    Article Title: Pharmacometabolomics via real-time breath analysis captures metabotypes of asthmatic children associated with salbutamol responsiveness
    Article Snippet: Finally, the centroid dataset was binned ±1 ppm using MATLAB’s ksdensity function.

    Article Title: Monitoring poultry social dynamics using colored tags: Avian visual perception, behavioral effects, and artificial intelligence precision.
    Article Snippet: This process specifically utilized the ksdensity function in MATLAB version R2023a, 9.14.0.2337262 (MathWorks, Inc., Massachusetts, USA), and the coordinates of the animals obtained during the 1 h test period.



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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