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In this paper we introduce the so-called Generalized Naive Bayes structure as an extension of the Naive Bayes structure. We give a new greedy algorithm that finds a good fitting Generalized Naive Bayes (GNB) probability distribution. We prove that th
Externí odkaz:
http://arxiv.org/abs/2408.15923
Autor:
Theiler, James
Bayesian priors are investigated for detecting targets of known spectral signature (but unknown strength) in cluttered backgrounds. A specific problem is the construction (or ``sculpting'') of a Bayesian prior that uniformly outperforms its non-Bayes
Externí odkaz:
http://arxiv.org/abs/2408.04572
Progress in neuroscience has provided unprecedented opportunities to advance our understanding of brain alterations and their correspondence to phenotypic profiles. With data collected from various imaging techniques, studies have integrated differen
Externí odkaz:
http://arxiv.org/abs/2407.21154
Autor:
Benitez, Edgar, Balaguer, Alvaro
In this study, the combined use of structural equation modeling (SEM) and Bayesian network modeling (BNM) in causal inference analysis is revisited. The perspective highlights the debate between proponents of using BNM as either an exploratory phase
Externí odkaz:
http://arxiv.org/abs/2407.18612
Autor:
Kurata, Sumito, Hirose, Kei
In the last two decades, sparse regularization methods such as the LASSO have been applied in various fields. Most of the regularization methods have one or more regularization parameters, and to select the value of the regularization parameter is es
Externí odkaz:
http://arxiv.org/abs/2407.16116
This paper studies two classes of sampling methods for the solution of inverse problems, namely Randomize-Then-Optimize (RTO), which is rooted in sensitivity analysis, and Langevin methods, which are rooted in the Bayesian framework. The two classes
Externí odkaz:
http://arxiv.org/abs/2406.16658
We provide a theoretical and computational investigation of the Gamma-Maximin method with soft revision, which was recently proposed as a robust criterion for pseudo-label selection (PLS) in semi-supervised learning. Opposed to traditional methods fo
Externí odkaz:
http://arxiv.org/abs/2405.15294
The Bayesian evidence, crucial ingredient for model selection, is arguably the most important quantity in Bayesian data analysis: at the same time, however, it is also one of the most difficult to compute. In this paper we present a hierarchical meth
Externí odkaz:
http://arxiv.org/abs/2405.07504
Autor:
Ernst, Philip A., Peskir, Goran
The Gapeev-Shiryaev conjecture (originating in Gapeev and Shiryaev (2011) and Gapeev and Shiryaev (2013)) can be broadly stated as follows: Monotonicity of the signal-to-noise ratio implies monotonicity of the optimal stopping boundaries. The conject
Externí odkaz:
http://arxiv.org/abs/2405.01685
Autor:
Adachi, Masaki, Hayakawa, Satoshi, Jørgensen, Martin, Hamid, Saad, Oberhauser, Harald, Osborne, Michael A.
Parallelisation in Bayesian optimisation is a common strategy but faces several challenges: the need for flexibility in acquisition functions and kernel choices, flexibility dealing with discrete and continuous variables simultaneously, model misspec
Externí odkaz:
http://arxiv.org/abs/2404.12219