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Showing 61 - 80 results of 290 for search '(( python practical implications ) OR ( ((python model) OR (python code)) implementation ))', query time: 0.22s Refine Results
  1. 61
    BSTPP: a python package for Bayesian spatiotemporal point processes

    BSTPP: a python package for Bayesian spatiotemporal point processes by Isaac Manring (20705955)

    Published 2025
    “…However, they are sometimes neglected due to the difficulty of implementing them. There is a lack of packages with the ability to perform inference for these models, particularly in python. …”
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  2. 62
    Configurations used in ALAAMs for undirected networks.

    Configurations used in ALAAMs for undirected networks. by Alex Stivala (8356257)

    Published 2024
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  3. 63
    Type II error rate from estimation of simulated outcomes using the EE algorithm.

    Type II error rate from estimation of simulated outcomes using the EE algorithm. by Alex Stivala (8356257)

    Published 2024
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  4. 64
    Comparison of MPNet and ALAAMEE software for ALAAM estimation.

    Comparison of MPNet and ALAAMEE software for ALAAM estimation. by Alex Stivala (8356257)

    Published 2024
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  5. 65
    Configurations used in ALAAMs for directed networks.

    Configurations used in ALAAMs for directed networks. by Alex Stivala (8356257)

    Published 2024
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  6. 66
    Type I error rate from estimation of simulated outcomes using the EE algorithm.

    Type I error rate from estimation of simulated outcomes using the EE algorithm. by Alex Stivala (8356257)

    Published 2024
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  7. 67
    Example ALAAMEE output.

    Example ALAAMEE output. by Alex Stivala (8356257)

    Published 2024
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  8. 68
    Type II error rate as the number of runs used for each sample is varied.

    Type II error rate as the number of runs used for each sample is varied. by Alex Stivala (8356257)

    Published 2024
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  9. 69
    Parameter estimates and estimated standard errors from the EE algorithm.

    Parameter estimates and estimated standard errors from the EE algorithm. by Alex Stivala (8356257)

    Published 2024
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  10. 70
    Network descriptive statistics for the Deezer networks.

    Network descriptive statistics for the Deezer networks. by Alex Stivala (8356257)

    Published 2024
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  11. 71
    Example ALAAMEE goodness-of-fit output.

    Example ALAAMEE goodness-of-fit output. by Alex Stivala (8356257)

    Published 2024
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  12. 72
    Illustration of the text transformation process before and after synonym replacement: The process begins with a sample initial text segment (Sample text before synonym replacement’), followed by a word frequency count (‘Counter’).

    Illustration of the text transformation process before and after synonym replacement: The process begins with a sample initial text segment (Sample text before synonym replacement’... by Enock Niyonkuru (21246879)

    Published 2025
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  13. 73
    Schematic of the approach: This schematic illustrates the entire workflow of the project.

    Schematic of the approach: This schematic illustrates the entire workflow of the project. by Enock Niyonkuru (21246879)

    Published 2025
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  14. 74
    Text Transformation Pipeline: An example of the multi-stage text transformation pipeline applied to a sample abstract (PMID: 30609739).

    Text Transformation Pipeline: An example of the multi-stage text transformation pipeline applied to a sample abstract (PMID: 30609739). by Enock Niyonkuru (21246879)

    Published 2025
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  15. 75
    Comparative analysis of WordNet replacement impact on data distribution.

    Comparative analysis of WordNet replacement impact on data distribution. by Enock Niyonkuru (21246879)

    Published 2025
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  16. 76
    Comparison of window size for embedding with Wordnet synonym replacement (WN) and without (PM). While Table 1 compared the effects of different values of using a window size of 10, this Table shows the results for three different window sizes at a =. Abbreviations are the same as for Table 1.

    Comparison of window size for embedding with Wordnet synonym replacement (WN) and without (PM). While Table 1 compared the effects of different values of using a window size of 10,... by Enock Niyonkuru (21246879)

    Published 2025
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  17. 77
    Non-biomedical word replacement algorithm: This algorithm outlines the process for replacing non-biomedical words in a corpus using WordNet.

    Non-biomedical word replacement algorithm: This algorithm outlines the process for replacing non-biomedical words in a corpus using WordNet. by Enock Niyonkuru (21246879)

    Published 2025
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  18. 78
    Comparison of mean interconcept distance for embedding with WordNet synonym replacement (WN) and without (PM). The initial number of unique concepts in the total corpus was 3,018,918. The Table summarizes results for different thresholds () and categories of concept/gene sets (M,B,K,G,P). Columns: : Replacement threshold; replaced: Unique Replaced Concepts; Category: M = MeSH, B = Biocarta, K = KEGG, G = GP(bp), P = PID; # sets: Number of concept/gene sets in the categories; #Concepts: number of concept vectors in the category; <i>WN better</i><i>: The count and percentage of concept/gene sets for which the mean interconcept distance was smaller for WN than for PM. “Winners” are shown in bold.;</i> <i>PM better</i><i>: Analogous to “WN better” but for PM.</i>

    Comparison of mean interconcept distance for embedding with WordNet synonym replacement (WN) and without (PM). The initial number of unique concepts in the total corpus was 3,018,9... by Enock Niyonkuru (21246879)

    Published 2025
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  19. 79
    Comparative analysis of WN and PM methodologies: Figure (a) displays the bar chart comparing WN and PM across five distinct concept sets (Methods), highlighting the number of concept sets where the cluster mean distance is significantly lower, indicative of superior embeddings.

    Comparative analysis of WN and PM methodologies: Figure (a) displays the bar chart comparing WN and PM across five distinct concept sets (Methods), highlighting the number of conce... by Enock Niyonkuru (21246879)

    Published 2025
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  20. 80
    Bursty model and Refractory model simulation results. (A) Bursty model simulation results ( samples). The production of nascent mRNA occurs in bursts and degrade after delay time <i>τ</i>. The burst frequency , the mean burst size and . (B) Refractory model simulation results ( samples). The gene has three states, , and , and can switch from one state to another. Moreover, nascent mRNA is produced only when the gene is in state . The parameters are , , , and <i>τ</i> = 1.

    Bursty model and Refractory model simulation results. (A) Bursty model simulation results ( samples). The production of nascent mRNA occurs in bursts and degrade after delay time <... by Ziyan Jin (21021976)

    Published 2025
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