Zobrazeno 1 - 10
of 139
pro vyhledávání: '"62-02"'
Autor:
Lachmann, Jon, Hubin, Aliaksandr
Bayesian Generalized Nonlinear Models (BGNLM) offer a flexible nonlinear alternative to GLM while still providing better interpretability than machine learning techniques such as neural networks. In BGNLM, the methods of Bayesian Variable Selection a
Externí odkaz:
http://arxiv.org/abs/2312.16997
Choosing the Fisher information as the metric tensor for a Riemannian manifold provides a powerful yet fundamental way to understand statistical distribution families. Distances along this manifold become a compelling measure of statistical distance,
Externí odkaz:
http://arxiv.org/abs/2306.01278
We propose a framework for fitting fractional polynomials models as special cases of Bayesian Generalized Nonlinear Models, applying an adapted version of the Genetically Modified Mode Jumping Markov Chain Monte Carlo algorithm. The universality of t
Externí odkaz:
http://arxiv.org/abs/2305.15903
Artificial neural networks (ANNs) are powerful machine learning methods used in many modern applications such as facial recognition, machine translation, and cancer diagnostics. A common issue with ANNs is that they usually have millions or billions
Externí odkaz:
http://arxiv.org/abs/2305.03395
Autor:
Hubin, Aliaksandr, Storvik, Geir
Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using a Bayesian app
Externí odkaz:
http://arxiv.org/abs/2305.00934
The variance-gamma (VG) distributions form a four-parameter family which includes as special and limiting cases the normal, gamma and Laplace distributions. Some of the numerous applications include financial modelling and distributional approximatio
Externí odkaz:
http://arxiv.org/abs/2303.05615
Autor:
Grigutis, Andrius
This article gives a probabilistic overview of the widely used method of default probability estimation proposed by K. Pluto and D. Tasche. There are listed detailed assumptions and derivation of the inequality where the probability of default is inv
Externí odkaz:
http://arxiv.org/abs/2303.06148
Autor:
Krinsman, William
This survey provides an overview of common applications, both implicit and explicit, of "tensors" and "tensor products" in the fields of data science and statistics. One goal is to reconcile seemingly distinct usages of the term "tensor" in the liter
Externí odkaz:
http://arxiv.org/abs/2210.16182
Autor:
Bochkina, Natalia
Publikováno v:
In: Belomestny, D., Butucea, C., Mammen, E., Moulines, E., Rei{\ss}, M., Ulyanov, V.V. (eds) Foundations of Modern Statistics. FMS 2019. Springer Proceedings in Mathematics & Statistics, vol 425. Springer
This is a review of asymptotic and non-asymptotic behaviour of Bayesian methods under model specification. In particular we focus on consistency, i.e. convergence of the posterior distribution to the point mass at the best parametric approximation to
Externí odkaz:
http://arxiv.org/abs/2204.13614
This article proposes a set of categories, each one representing a particular distillation of important statistical ideas. Each category is labeled a "sense" because we think of these as essential in helping every statistical mind connect in construc
Externí odkaz:
http://arxiv.org/abs/2204.05313