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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, 2026-09
    90/100 stars

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    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: Enhanced cerebral blood flow similarity of the somatomotor network in chronic insomnia: Transcriptomic decoding, gut microbial signatures and phenotypic roles.
    Article Snippet: Briefly, the values of all vertices within each ROI were extracted for each morphological index, for which a probability density estimate was computed using a MATLAB function, ksdensity.

    Article Title: Domain-decomposed Bayesian inversion based on local Karhunen-Loève expansions
    Article Snippet: In Fig. 10, the marginal densities of the posterior distribution are obtained using kernel density estimation (the MATLAB function ksdensity is used) with local posterior samples generated by DD-MCMC (line 19 of Algorithm 2), and each Gaussian approximation is a Gaussian distribution with the mean and the covariance (or the variance) estimated using the local posterior samples.

    Article Title: Cerebellar activity predicts vocalization in fruit bats.
    Article Snippet: To analyze neuronal spiking around the produced vocalizations, we computed the spiking probability of each unit to each vocalization event from 500 ms prior until 500 ms after its onset in 5 ms bins (Matlab function ‘‘ksdensity’’).

    Article Title: Morphological Brain Networks of White Matter: Mapping, Evaluation, Characterization, and Application.
    Article Snippet: Then, values of WM volume and deformation were separately extracted from all voxels within each ROI to derive regional probability density functions (MATLAB function, ksdensity).

    Article Title: Flexible neural population dynamics govern the speed and stability of sensory encoding in mouse visual cortex
    Article Snippet: The probability density estimates are obtained using the Matlab function ksdensity .

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

    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.



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    https://www.bioz.com/product/matlab+function+ksdensity/pmc11999189-80-21-19
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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