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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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    other:

    Article Title: Interannual Ural Atmospheric Variability Under Slow‐Varying Soil Moisture and Snow Cover: Three Quasi‐Equilibrium States
    Article Snippet: We use the MATLAB function “ksdensity” to calculate the joint PDF of two variables (refer to https://www.mathworks.com/help/stats/ksdensity.html).

    Article Title: Visualizing PIEZO1 localization and activity in hiPSC-derived single cells and organoids with HaloTag technology.
    Article Snippet: We used the “ksdensity” function in MATLAB with a “normal” kernel having a bandwidth of [2.503 μm, 3.661 μm] for the distances to the lumen and outer edge, respectively.

    Article Title: Early and late place cells during postnatal development of the hippocampus
    Article Snippet: We used the MATLAB function “ ksdensity ” for density estimation on the data points, creating a density estimate for each point.

    Article Title: Early and late place cells during postnatal development of the hippocampus.
    Article Snippet: We used the MATLAB function “ksdensity” for density estimation on the data points, creating a density estimate for eachpoint.

    Article Title: Striatal neural ensemble codes for voluntary locomotor and involuntary dyskinetic movements
    Article Snippet: This KDE was calculated using MATLAB’s default ksdensity function, which uses a Gaussian kernel to estimate the probability density function of the data.

    Article Title: Early and late place cells during postnatal development of the hippocampus
    Article Snippet: We identified the location of max density in this scatter plot using the “ ksdensity ” function in MATLAB.

    Article Title: Investigating the predictive models of efficacy of accelerated neuronavigation-guided rTMS for suicidal depression based on multimodal large-scale brain networks
    Article Snippet: Specifically, values for each morphological index within each ROI were extracted to estimate a probability density function using a MATLAB function, ksdensity.

    Generated:

    Article Title: ON-OFF neuromorphic ISING machines using Fowler-Nordheim annealers.
    Article Snippet: .. The probability density function (pdf) was generated using ksdensity, the built-in MATLAB function. ..



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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