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kernel density estimation functions (kde; matlab function 'ksdensity)  (MathWorks Inc)


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    MathWorks Inc kernel density estimation functions (kde; matlab function 'ksdensity)
    Kernel Density Estimation Functions (Kde; 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/pmc11421896-76-7-12
    Average 90 stars, based on 1 article reviews
    kernel density estimation functions (kde; matlab function 'ksdensity) - by Bioz Stars, 2026-10
    90/100 stars

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    Modification:

    Article Title: White matter connections within the central sulcus subserving the somato-cognitive action network
    Article Snippet: .. The region of highest probability of evoking MEPs was computed using a modified in-house version of probability kernel density estimation (PDE analysis) implemented in MATLAB. ..

    other:

    Article Title: Comparison of conventional and radiomics-based analysis of myocardial infarction using multimodal non-linear optical microscopy
    Article Snippet: In order to select the most quantitative radiomic features for further analysis, we performed a kernel density estimation (KDE) in Matlab using the functions described in .

    Article Title: Quality Issues in Kinematic Traces from Three Head Impact Sensors in Boxing: Prevalence, Effects, and Implications for Exposure Assessment.
    Article Snippet: Purpose When used in-vivo or in biofidelic environments, many head impact sensors have shown limitations related to the quality and validity of the kinematics measured.. The objectives were to assess the quality of kinematic traces from three head impact sensors, determine the effects of signal quality on peak accelerations, and compare measurements across sensors.. Methods Head impacts were collected with instrumented mouthguards, skin patches, and headgear patches during boxing sparring.

    Article Title: The Role of Striatum in Controlling Waiting during Reactive and Self-Timed Behaviors
    Article Snippet: The probability density function of hold duration was estimated using kernel density estimation (ksdensity in MATLAB) with a bandwidth of 0.075 and a bin width of 0.05 s. Event timing was recorded with a temporal resolution of 10 ms. For postlesion analyses, sessions with fewer than 50 responses were combined with subsequent sessions to ensure sufficient sample size for statistical analysis (e.g., A , B , 6 A – D , 7, 8 B – E , 9 B – F ).

    Generated:

    Article Title: Spatial differences and formation mechanisms of innovation ecosystem dynamic operational efficiency along the yellow river
    Article Snippet: .. Three-dimensional kernel density evolution maps of the overall Yellow River Basin (Fig. ) and the upstream (Fig. ), midstream (Fig. ), and downstream (Fig. ) of the Innovation Ecosystem dynamic operational efficiency of the Innovation Ecosystem were generated via MATLAB 2023. ..



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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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    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