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Bridging Machine Learning and Algorithmic Information Theory, Part VII: Algorithmic Information Kernels and Kernel Discrepancies on Countable Spaces [PDF]
EntropyCompression-based dissimilarities such as the normalized compression distance are widely used, but direct exponentiation need not produce a positive semidefinite kernel.
Boumediene Hamzi +2 more
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Ergodicity and regime recoverability in finite Markov-modulated random walks [PDF]
Scientific ReportsWe study a finite Markov-modulated random walk (MMRW) on a one-dimensional lattice with reflecting boundaries, where a hidden finite-state Markov environment $$E_t$$ selects at each time step a regime-specific random walk kernel for the position process $
Arina Pambukyan +2 more
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EEG-Based Supported Diagnosis of ADHD Using Subject-Specific HMMs and Stationary RKHS Embeddings [PDF]
SensorsElectroencephalography (EEG) provides a non-invasive means of supporting Attention-Deficit/Hyperactivity Disorder (ADHD) assessment. Nonetheless, existing pipelines often rely on handcrafted descriptors, segment-wise decisions, or deep architectures with
Leonardo Lopez-Ortiz +4 more
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Developing a Strategy for Buying and Selling Stocks Based on Semi-Parametric Markov Switching Time Series Models [PDF]
Iranian Journal of Finance, 2021 The modeling of strategies for buying and selling in Stock Market Investment has been the object of numerous advances and uses in economic studies, both theoretically and empirically.
Hossein Naderi +3 more
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Decomposition of Finitely Additive Markov Chains in Discrete Space
Mathematics, 2022 In this study, we consider general Markov chains (MC) defined by a transition probability (kernel) that is finitely additive. These Markov chains were constructed by S. Ramakrishnan within the concepts and symbolism of game theory.
Alexander Zhdanok, Anna Khuruma
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Approximate Bayesian Computation for Discrete Spaces
Entropy, 2021 Many real-life processes are black-box problems, i.e., the internal workings are inaccessible or a closed-form mathematical expression of the likelihood function cannot be defined.
Ilze A. Auzina, Jakub M. Tomczak
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Maximal asymmetry of bivariate copulas and consequences to measures of dependence
Dependence Modeling, 2022 In this article, we focus on copulas underlying maximal non-exchangeable pairs (X,Y)\left(X,Y) of continuous random variables X,YX,Y either in the sense of the uniform metric d∞{d}_{\infty } or the conditioning-based metrics Dp{D}_{p}, and analyze their ...
Griessenberger Florian +1 more
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Frontiers in Applied Mathematics and Statistics, 2022 Performing Markov chain Monte Carlo parameter estimation on complex mathematical models can quickly lead to endless searching through highly multimodal parameter spaces.
Graham West +2 more
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Dependence Modeling, 2023 Working with shuffles, we establish a close link between Kendall’s τ\tau , the so-called length measure, and the surface area of bivariate copulas and derive some consequences.
Sánchez Juan Fernández +1 more
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PAC-Bayes Bounds on Variational Tempered Posteriors for Markov Models
Entropy, 2021 Datasets displaying temporal dependencies abound in science and engineering applications, with Markov models representing a simplified and popular view of the temporal dependence structure.
Imon Banerjee +2 more
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