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matlab function fitdist  (MathWorks Inc)


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    MathWorks Inc matlab function fitdist
    Matlab Function Fitdist, 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/fitdist+function/pm39951648-333-9-7
    Average 90 stars, based on 1 article reviews
    matlab function fitdist - by Bioz Stars, 2026-10
    90/100 stars

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    Article Title: Stochastic resonance in sparse neuronal network: functional role of ongoing activity to detect weak sensory input in awake auditory cortex of rat.
    Article Snippet: A firing distribution of each unit was lognormally fitted with the MATLAB function “fitdist,” and the σ was estimated across the units in all 5 rats.

    Article Title: Descending neurons of the hoverfly respond to pursuits of artificial targets.
    Article Snippet: We used Matlab’s fitdist to fit the ISI distribution with either a gamma or a Poisson function, as these two are often used to described spike-time statistics.70,71 We then used Matlab’s gfit to calculate the root mean squared error (RMSE) between the fit and the ISI data and found that the gamma function provided a better fit than the Poisson function (Figure S3).

    Article Title: Blind Image Quality Assessment by Gaussian Mixture Distribution
    Article Snippet: In the field of image quality assessment (IQA), researchers have been studying the mean opinion score (MOS) of image quality for decades.. They focus on developing IQA methods with the help of MOS without using the potential of the distribution of opinion scores (DOS).. We find that the Gaussian mixture distribution (GMD) can more accurately describe the DOS of image quality on SJTU IQSD and KonIQ-10K databases compared to some traditional distributions.

    Article Title: Astrocytic metabolic control of orexinergic activity in the lateral hypothalamus regulates sleep and wake architecture
    Article Snippet: To compare the CTRL vs KD conditions, the values for each group for a given state were subsequently used to compute the Kernel probability distribution (pd) using the fitdist.mat function in MATLAB; the resulting Kernel pd was used to reconstruct a smaller dataset (considering the sensitivity of the Kolmogorov-Smirnov test to the sample size of the analyzed vectors; n=400) using the random.mat function in MATLAB and the pd as an input.

    Article Title: Cost-effectiveness of the rural lifestyle eating and activity program (Rural LEAP)
    Article Snippet: MatLab’s “fitdist” function was used to identify the best fitting probability distributions of key parameters from the base-case [ ].

    Article Title: Horizontal, vertical, and torsional optokinetic responses and their adaptations in fish.
    Article Snippet: Eye movements in vertebrates moving around stabilize retinal images, achieved through the vestibuloocular reflex (VOR) and the optokinetic response (OKR).. Although VOR compensates for head velocity, its effectiveness declines with prolonged motion, necessitating the OKR.. This study explores the three-dimensional (3-D) nature of the OKR in goldfish, focusing on horizontal (H), vertical (V), and torsional (T) responses and their adaptation.

    Article Title: Astrocytic metabolic control of orexinergic activity in the lateral hypothalamus regulates sleep and wake architecture.
    Article Snippet: To compare the CTRL vs KD conditions, the values for each group for a given state were subsequently used to compute the Kernel probability distribution (pd) using the fitdist.mat function in MATLAB; the resulting Kernel pd was used to reconstruct a smaller dataset (considering the sensitivity of the Kolmogorov–Smirnov test to the sample size of the analyzed vectors; n = 400) using the random.mat function in MATLAB and the pd as an input.

    Article Title: Artificial neural network for inferring radiative property variations across advanced high strength steel coils
    Article Snippet: This work proposes a parametric, surrogate artificial neural network (ANN) for inferring the radiative properties of advanced high-strength steel (AHSS) alloys in their cold-rolled and pre-annealed condition using surface optical imagery and topography parameters.. The ANN proposed in this study may serve as a complementary method to physics-based models for the in-situ prediction of radiative property variations across cold-rolled AHSS coils with emerging on-line strip imaging tools.. The ANN is trained and tested on empirical data from 164 industrially processed AHSS samples whose spectral emissivities and surface topographies are analysed using FTIR spectroscopy and optical microscope imagery with 3D depth mapping, respectively.



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