Results 61 to 70 of about 830,323 (220)
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 In this paper, we propose a novel hybrid discriminative generative model by integrating a modified version of hidden Markov model (HMM), multivariate Beta-based HMM with support vector machine (SVM). We apply Fisher Kernel to define decision boundary and
Narges Manouchehri, Nizar Bouguila
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Regularity of transition semigroups associated to a 3D stochastic Navier-Stokes equation
, 2006 A 3D stochastic Navier-Stokes equation with a suitable non degenerate additive noise is considered. The regularity in the initial conditions of every Markov transition kernel associated to the equation is studied by a simple direct approach. A by-product
Flandoli, F., Romito, M.
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Markov Kernels Local Aggregation for Noise Vanishing Distribution Sampling
SIAM Journal on Mathematics of Data Science, 2022 A novel strategy that combines a given collection of $\pi$-reversible Markov kernels is proposed. At each Markov transition, one of the available kernels is selected via a state-dependent probability distribution. In contrast to random-scan type approaches that assume a constant (i.e.
Maire, Florian, Vandekerkhove, Pierre
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Estimates of Dirichlet heat kernel for symmetric Markov processes [PDF]
Stochastic Processes and their Applications, 2015 We consider a large class of symmetric pure jump Markov processes dominated by isotropic unimodal L\'evy processes with weak scaling conditions. First, we establish sharp two-sided heat kernel estimates for these processes in $C^{1,1}$ open sets.
T. Grzywny, Kyung-Youn Kim, P. Kim
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Practical Markov Boundary Learning without Strong Assumptions
AAAI Conference on Artificial Intelligence, 2023 Theoretically, the Markov boundary (MB) is the optimal solution for feature selection. However, existing MB learning algorithms often fail to identify some critical features in real-world feature selection tasks, mainly because the strict assumptions of ...
Xingyu Wu +3 more
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The Category of Markov Kernels
Electronic Notes in Theoretical Computer Science, 1999 AbstractMarkov kernels are fundamental objects in probability theory. One can define a category based on Markov kernels which has many of the formal properties of the ordinary category of relations. In the present paper we will examine the categorical properties of Markov kernels and stress the analogies and differences with the category of relations ...
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Searching for efficient Markov chain Monte Carlo proposal kernels [PDF]
Proceedings of the National Academy of Sciences, 2013 SignificanceBayesian statistics is widely used in various branches of sciences; its main computational method is the Markov chain Monte Carlo (MCMC) algorithm, which is used to simulate a sample on the computer, on which all Bayesian inference is based.
Yang, Z, Rodríguez, CE
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Node localization algorithm based on kernel function and Markov chains
Tongxin xuebao, 2010 To position indoor objects accurately and robustly,a novel node localization based on kernel function and Markov chains was presented,which employs Bayesian filter framework and radio fingerprinting technology.It uses kernel function to construct ...
ZHAO Fang1 +3 more
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On bivariate Archimedean copulas with fractal support
Dependence ModelingDue to their simple analytic form (bivariate) Archimedean copulas are usually viewed as very smooth and handy objects, which should distribute mass in a fairly regular and certainly not in a pathological way. Building upon recently established results on
Sánchez Juan Fernández +1 more
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Connecting the latent multinomial
, 2015 Link et al. (2010) define a general framework for analyzing capture-recapture data with potential misidentifications. In this framework, the observed vector of counts, $y$, is considered as a linear function of a vector of latent counts, $x$, such that ...
Bonner, Simon J., Schofield, Matthew R.
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