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mmb: Arbitrary Dependency Mixed Multivariate Bayesian Models

DOI: 10.5281/zenodo.4046002

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  • Title:
    mmb: Arbitrary Dependency Mixed Multivariate Bayesian Models
  • Author: Hönel, Sebastian
  • Subjects: bayes classifier ; CRAN ; Information and software visualization ; Informations- och programvisualisering ; kernel density estimation ; neighborhood search ; R package ; regression models ; Statistics/Econometrics ; Statistik
  • Description: The challenges posed by dependent variables in classification and regression using techniques based on Bayes' theorem are often avoided by assuming strong independence between the variables. Hence, such techniques are called naive. While analytical solutions supporting classification on arbitrary numbers of discrete and continuous random variables exist, practical solutions are scarce. This is true for Bayesian models that support regression and neighborhood search, likewise. To overcome the naive independence assumption, those models analytically resolve the dependencies using empirical joint conditional probabilities and joint conditional probability densities. These are obtained by posterior probabilities of the dependent variable after segmenting the dataset for each random variable's value. We demonstrate the advantages of these models: (i) they are deterministic, i.e., no randomization or weights and, hence, no training is required; (ii) each random variable may have an arbitrary probability distribution; and (iii) online learning is effortlessly possible. We evaluate a few Bayesian models empirically and assess their performance by comparing them against well-established classifiers and regression models, using well-known datasets. In classification, our models can outperform others in certain settings. In regression, our models deliver respectable performance without leading the field. Additionally, we provide a true statistical distance metric and a neighborhood search based on such models.
  • Creation Date: 2020
  • Language: English
  • Identifier: DOI: 10.5281/zenodo.4046002
  • Source: SWEPUB Freely available online
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