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A Fully Bayesian Inference Approach for Multivariate McDonald's Beta Mixture Model with Feature Selection 9

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A Fully Bayesian Inference Approach for Multivariate McDonal...

9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Forouzanfar, Darya Manouchehri, Narges Bouguila, Nizar Karolinska Institute Algorithmic Dynamics Lab Unit of Computational Medicine Stockholm171 77 Sweden Concordia University Concordia Institute for Information Systems Engineering Montreal Canada

ISBN: (纸本)9798350311402

Mixture models are widely used in unsupervised machine learning applications where annotating a large amount of data is not feasible. They have succeeded in various real-world problems, including medical applications, human activity recognition, and anomaly detection. This paper proposes a fully Bayesian analysis of the multivariate McDonald's Beta mixture model (McDBMM) using Gibbs sampling method and Metropolis-Hastings to estimate parameters. In addition, we integrated a feature selection technique which simultaneously determines the most relevant features for our mixture model. This allows for the simultaneous selection of the most relevant features, improving the accuracy and efficiency of the unsupervised learning process. Our approach is evaluated on challenging applications, including lung cancer image analysis and human activity recognition. Experimental results indicate that our proposed method is an effective solution compared to the Gaussian mixture model (GMM). © 2023 IEEE.

关键词： Medical applications

algorithmically probable mutations reproduce aspects of evolution such as convergence rate, genetic memory, and modularity

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arXiv 2017年

作者： Hernández-Orozco, Santiago Kiani, Narsis A. Zenil, Hector Mexico Algorithmic Dynamics Lab Unit of Computational Medicine SciLifeLab Department of Medicine Solna Centre for Molecular Medicine Stockholm Sweden Algorithmic Nature Group LABORES Paris France

Natural selection explains how life has evolved over millions of years from more primitive forms. The speed at which this happens, however, has sometimes defied formal explanations when based on random (uniformly distributed) mutations. Here we investigate the application of a simplicity bias based on a natural but algorithmic distribution of mutations (no recombination) in various examples, particularly binary matrices in order to compare evolutionary convergence rates. Results both on synthetic and on small biological examples indicate an accelerated rate when mutations are not statistical uniform but algorithmic uniform. We show that algorithmic distributions can evolve modularity and genetic memory by preservation of structures when they first occur sometimes leading to an accelerated production of diversity but also population extinctions, possibly explaining naturally occurring phenomena such as diversity explosions (e.g. the Cambrian) and massive extinctions (e.g. the End Triassic) whose causes are currently a cause for debate. The natural approach introduced here appears to be a better approximation to biological evolution than models based exclusively upon random uniform mutations, and it also approaches a formal version of open-ended evolution based on previous formal results. These results validate some suggestions in the direction that computation may be an equally important driver of evolution. We also show that inducing the method on problems of optimization, such as genetic algorithms, has the potential to accelerate convergence of artificial evolutionary algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： computational complexity

A Fully Bayesian Inference Approach for Multivariate McDonald's Beta Mixture Model with Feature Selection

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A Fully Bayesian Inference Approach for Multivariate McDonal...

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Darya Forouzanfar Narges Manouchehri Nizar Bouguila Concordia University Concordia Institute for Information Systems Engineering Montreal Canada Algorithmic Dynamics Lab Unit of Computational Medicine Karolinska Institute Stockholm Sweden

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Reprogramming matter, life, and purpose

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arXiv 2017年

作者： Zenil, Hector Algorithmic Dynamics Lab Unit of Computational Medicine Center for Molecular Medicine SciLifeLab Karolinska Institute Stockholm Sweden Department of Computer Science University of Oxford Oxford United Kingdom Algorithmic Nature Group LABORES Paris France

Reprogramming matter may sound far-fetched, but we have been doing it with increasing power and staggering efficiency for at least 60 years, and for centuries we have been paving the way toward the ultimate reprogrammed fate of the universe, the vessel of all programs. How will we be doing it in 60 years’ time and how will it impact life and the purpose both of machines and of humans? Copyright © 2017, The Authors. All rights reserved.

关键词： Parallel processing systems

A Nonparametric Bayesian Framework for Multivariate Libby-Novick Beta Mixture Models

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A Nonparametric Bayesian Framework for Multivariate Libby-No...

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Niloufar Samiee Narges Manouchehri Nizar Bouguila Concordia Institute for Information Systems Engineering (CIISE) Concordia University Montreal Canada Algorithmic Dynamics Lab Unit of Computational Medicine Karolinska Institute Stockholm Sweden

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This work presents a nonparametric Bayesian approach that utilizes a mixture of multivariate Libby-Novick Beta distributions to address clustering challenges. When using mixtures, model selection is a significant obstacle. As a solution to this problem, we extend the finite Libby-Novick Beta mixture model (FLNBMM)to the infinite case. This enables us to accurately represent the data distribution by accommodating an unspecified number of mixture components. We develop a Bayesian inference strategy based on Markov Chain Monte Carlo to estimate the posterior distribution, which provides strong power and flexibility for modeling and analyzing complicated data. Our suggested method’s effectiveness is assessed on three applications and contrasted with that of FLNBMM, the infinite Gaussian mixture model (IGMM), and the finite Gaussian mixture model (FGMM) to show the efficacy of our methodology. It is evident from the results that our proposed model is a good alternative.

关键词： Analytical models Monte Carlo methods Process control Mixture models Data models Bayes methods Information technology Gaussian mixture model

Maximum Likelihood-Based Estimation of Finite Multivariate Libby-Novick Beta Mixture Models in Medical Applications

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Maximum Likelihood-Based Estimation of Finite Multivariate L...

IEEE International Conference on Industrial Technology (ICIT)

作者： Niloufar Samiee Narges Manouchehri Nizar Bouguila Concordia Institute for Information Systems Engineering (CIISE) Concordia University Montreal QC Canada Algorithmic Dynamics Lab Unit of Computational Medicine Karolinska Institute Stockholm Sweden

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Compression is comprehension, and the unreasonable effectiveness of digital computation in the natural world

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arXiv 2019年

作者： Zenil, Hector Oxford Immune Algorithmics Oxford University Innovation Oxford United Kingdom Algorithmic Dynamics Lab Unit of Computational Medicine Center for Molecular Medicine Karolinska Institute Stockholm Sweden Algorithmic Nature Group LABORES for the Natural and Digital Sciences Paris France

Chaitin’s work, in its depth and breadth, encompasses many areas of scientific and philosophical interest. It helped establish the accepted mathematical concept of randomness, which in turn is the basis of tools that I have developed to justify and quantify what I think is clear evidence of the algorithmic nature of the world. To illustrate the concept I will establish novel upper bounds of algorithmic randomness for elementary cellular automata. I will discuss how the practice of science consists in conceiving a model that starts from certain initial values, running a computable instantiation, and awaiting a result in order to determine where the system may be in a future state—in a shorter time than the time taken by the actual unfolding of the phenomenon in question. If a model does not comply with all or some of these requirements it is traditionally considered useless or even unscientific, so the more precise and faster the better. A model is thus better if it can explain more with less, which is at the core of Chaitin’s "compression is comprehension". I will pursue these questions related to the random versus possibly algorithmic nature of the world in two directions, drawing heavily on the work of Chaitin. I will also discuss how the algorithmic approach is related to the success of science at producing models of the world, allowing computer simulations to better understand it and make more accurate predictions and interventions. Copyright © 2019, The Authors. All rights reserved.

关键词： Philosophical aspects

Training-free measures based on algorithmic probability identify high nucleosome occupancy in DNA sequences

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arXiv 2017年

作者： Zenil, Hector Minary, Peter Algorithmic Dynamics Lab Unit of Computational Medicine SciLifeLab Centre for Molecular Medicine Karolinska Institute Stockholm Sweden Department of Computer Science University of Oxford Oxford United Kingdom Algorithmic Nature Group LABORES for the Natural and Digital Sciences Paris France

We introduce and study a set of training-free methods of information-theoretic and algorithmic complexity nature applied to DNA sequences to identify their potential capabilities to determine nucleosomal binding sites. We test our measures on well-studied genomic sequences of different sizes drawn from different sources. The measures reveal the known in vivo versus in vitro predictive discrepancies and uncover their potential to pinpoint (high) nucleosome occupancy. We explore different possible signals within and beyond the nucleosome length and find that complexity indices are informative of nucleosome occupancy. We compare against the gold standard (Kaplan model) and find similar and complementary results with the main difference that our sequence complexity approach. For example, for high occupancy, complexity-based scores outperform the Kaplan model for predicting binding representing a significant advancement in predicting highest nucleosome occupancy following a training-free approach. Copyright © 2017, The Authors. All rights reserved.

关键词： DNA sequences

Symmetry and algorithmic complexity of polyominoes and polyhedral graphs

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arXiv 2018年

作者： Zenil, Hector Kiani, Narsis A. Tegnér, Jesper Algorithmic Dynamics Lab Centre for Molecular Medicine Karolinska Institute Stockholm Sweden Unit of Computational Medicine Department of Medicine Karolinska Institute Stockholm Sweden Science for Life Laboratory SciLifeLab Stockholm Sweden Algorithmic Nature Group LABORES for the Natural and Digital Sciences Paris France Saudi Arabia

We introduce a definition of algorithmic symmetry able to capture essential aspects of geometric symmetry. We review, study and apply a method for approximating the algorithmic complexity (also known as Kolmogorov-Chaitin complexity) of graphs and networks based on the concept of algorithmic Probability (AP). AP is a concept (and method) capable of recursively enumerate all properties of computable (causal) nature beyond statistical regularities. We explore the connections of al- gorithmic complexity|both theoretical and numerical| with geometric properties mainly symmetry and topology from an (algorithmic) information- theoretic perspective. We show that approximations to algorithmic com- plexity by lossless compression and an algorithmic Probability-based method can characterize properties of polyominoes, polytopes, regular and quasi- regular polyhedra as well as polyhedral networks, thereby demonstrating its profiling capabilities. Copyright © 2018, The Authors. All rights reserved.

关键词： Topology