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exponential and generalized pareto random number generators  (MathWorks Inc)


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    MathWorks Inc exponential and generalized pareto random number generators
    ( a ) Seven ensembles of networks of size N =1,500 and different topologies exhibit remarkably different convergence fractions (CFs). Ensembles are characterized by the out- and in- degree distributions of the adjacency matrix T : ‘SF', scale free distribution; ‘Exp', <t>exponential</t> distribution; ‘Binom', Binomial distribution. ( b ) CF as a function of network size for the same ensembles of ( a ) with matching colours. N =1,500, y =0, g 0 =10, D =10 −3 . Parameters for degree distributions: SF, ( a =1, γ =2.4); Binom, ; Exp, ( β =3.5).
    Exponential And Generalized Pareto Random Number Generators, 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/exponential+and+generalized+pareto+random+number+generators/pmc05413947-135-12-12?v=MathWorks+Inc
    Average 90 stars, based on 1 article reviews
    exponential and generalized pareto random number generators - by Bioz Stars, 2026-07
    90/100 stars

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    1) Product Images from "Exploratory adaptation in large random networks"

    Article Title: Exploratory adaptation in large random networks

    Journal: Nature Communications

    doi: 10.1038/ncomms14826

    ( a ) Seven ensembles of networks of size N =1,500 and different topologies exhibit remarkably different convergence fractions (CFs). Ensembles are characterized by the out- and in- degree distributions of the adjacency matrix T : ‘SF', scale free distribution; ‘Exp', exponential distribution; ‘Binom', Binomial distribution. ( b ) CF as a function of network size for the same ensembles of ( a ) with matching colours. N =1,500, y =0, g 0 =10, D =10 −3 . Parameters for degree distributions: SF, ( a =1, γ =2.4); Binom, ; Exp, ( β =3.5).
    Figure Legend Snippet: ( a ) Seven ensembles of networks of size N =1,500 and different topologies exhibit remarkably different convergence fractions (CFs). Ensembles are characterized by the out- and in- degree distributions of the adjacency matrix T : ‘SF', scale free distribution; ‘Exp', exponential distribution; ‘Binom', Binomial distribution. ( b ) CF as a function of network size for the same ensembles of ( a ) with matching colours. N =1,500, y =0, g 0 =10, D =10 −3 . Parameters for degree distributions: SF, ( a =1, γ =2.4); Binom, ; Exp, ( β =3.5).

    Techniques Used:

    Solid lines depict stretched exponential fits. ( a ) Probability density distribution (PDF) of convergence time for three topological ensembles. ( b ) PDFs after deleting the 8 largest hubs (red) or the same number of randomly-chosen nodes (light blue) from the SF-Binom ensemble. All ensembles have N =1,000, y =0, g 0 =10, and D =10 −3 . Degree distribution parameters: SF, ( a =1, γ =2.4); Binom, ; Exp, ( ).
    Figure Legend Snippet: Solid lines depict stretched exponential fits. ( a ) Probability density distribution (PDF) of convergence time for three topological ensembles. ( b ) PDFs after deleting the 8 largest hubs (red) or the same number of randomly-chosen nodes (light blue) from the SF-Binom ensemble. All ensembles have N =1,000, y =0, g 0 =10, and D =10 −3 . Degree distribution parameters: SF, ( a =1, γ =2.4); Binom, ; Exp, ( ).

    Techniques Used:



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    exponential and generalized pareto random number generators  (MathWorks Inc)
    90
    MathWorks Inc exponential and generalized pareto random number generators
    ( a ) Seven ensembles of networks of size N =1,500 and different topologies exhibit remarkably different convergence fractions (CFs). Ensembles are characterized by the out- and in- degree distributions of the adjacency matrix T : ‘SF', scale free distribution; ‘Exp', <t>exponential</t> distribution; ‘Binom', Binomial distribution. ( b ) CF as a function of network size for the same ensembles of ( a ) with matching colours. N =1,500, y =0, g 0 =10, D =10 −3 . Parameters for degree distributions: SF, ( a =1, γ =2.4); Binom, ; Exp, ( β =3.5).
    Exponential And Generalized Pareto Random Number Generators, 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/exponential+and+generalized+pareto+random+number+generators/pmc05413947-135-12-12?v=MathWorks+Inc
    Average 90 stars, based on 1 article reviews
    exponential and generalized pareto random number generators - by Bioz Stars, 2026-07
    90/100 stars
    		  Buy from Supplier

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    ( a ) Seven ensembles of networks of size N =1,500 and different topologies exhibit remarkably different convergence fractions (CFs). Ensembles are characterized by the out- and in- degree distributions of the adjacency matrix T : ‘SF', scale free distribution; ‘Exp', exponential distribution; ‘Binom', Binomial distribution. ( b ) CF as a function of network size for the same ensembles of ( a ) with matching colours. N =1,500, y =0, g 0 =10, D =10 −3 . Parameters for degree distributions: SF, ( a =1, γ =2.4); Binom, ; Exp, ( β =3.5).

    Journal: Nature Communications

    Article Title: Exploratory adaptation in large random networks

    doi: 10.1038/ncomms14826

    Figure Lengend Snippet: ( a ) Seven ensembles of networks of size N =1,500 and different topologies exhibit remarkably different convergence fractions (CFs). Ensembles are characterized by the out- and in- degree distributions of the adjacency matrix T : ‘SF', scale free distribution; ‘Exp', exponential distribution; ‘Binom', Binomial distribution. ( b ) CF as a function of network size for the same ensembles of ( a ) with matching colours. N =1,500, y =0, g 0 =10, D =10 −3 . Parameters for degree distributions: SF, ( a =1, γ =2.4); Binom, ; Exp, ( β =3.5).

    Article Snippet: Exponential and Scale-free sequences are implemented by a discretization of the continuous MATLAB Exponential and Generalized Pareto random number generators with parameters k =1/( γ −1), σ = a /( γ −1) and θ = a .

    Techniques:

    Solid lines depict stretched exponential fits. ( a ) Probability density distribution (PDF) of convergence time for three topological ensembles. ( b ) PDFs after deleting the 8 largest hubs (red) or the same number of randomly-chosen nodes (light blue) from the SF-Binom ensemble. All ensembles have N =1,000, y =0, g 0 =10, and D =10 −3 . Degree distribution parameters: SF, ( a =1, γ =2.4); Binom, ; Exp, ( ).

    Journal: Nature Communications

    Article Title: Exploratory adaptation in large random networks

    doi: 10.1038/ncomms14826

    Figure Lengend Snippet: Solid lines depict stretched exponential fits. ( a ) Probability density distribution (PDF) of convergence time for three topological ensembles. ( b ) PDFs after deleting the 8 largest hubs (red) or the same number of randomly-chosen nodes (light blue) from the SF-Binom ensemble. All ensembles have N =1,000, y =0, g 0 =10, and D =10 −3 . Degree distribution parameters: SF, ( a =1, γ =2.4); Binom, ; Exp, ( ).

    Article Snippet: Exponential and Scale-free sequences are implemented by a discretization of the continuous MATLAB Exponential and Generalized Pareto random number generators with parameters k =1/( γ −1), σ = a /( γ −1) and θ = a .

    Techniques: