Bayesian estimation is a powerful theoretical paradigm for the operation of quantum sensors. However, the Bayesian method for statistical inference generally suffers from demanding calibration requirements that
Such a reliability estimation is a well-known problem in machine learning. For neural networks the solutions vary from the calibration of neural network classifiers79,80,81,82, to using an ensemble of neural networks and obtaining an uncertainty from the prediction spread83, to fully modelling the...
An efficient sampling algorithm for estimation is presented, along with simulation studies that compare the proposed approach with Q-learning.doi:10.1080/01621459.2017.1340887Murray, Thomas AYuan, YingThall, Peter FJournal of the American Statistical Association...
Machine Learning methods for Quality-of-Transmission estimation 7.3.2.1 Domain adaptation techniques In the following, three DA techniques applied in [45] are presented: Bayesian Updating (BU), Feature Augmentation (FA) [48] and CORrelation ALignment (CORAL) [49]. Bayesian Updating (BU) is a su...
(1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (pp. 1137–1143). San Mateo, CA: Morgan Kaufmann. Google Scholar Kohavi, R. (1996). Scaling up the accuracy...
machine-learningbayesian-inference UpdatedApr 7, 2025 C# janosh/awesome-normalizing-flows Sponsor Star1.5k Code Issues Pull requests Discussions Awesome resources on normalizing flows. machine-learningawesome-listbayesian-inferenceautoregressivevariational-inferencedensity-estimationnormalizing-flowsbayesian-neural-ne...
This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. The book also covers the fundamentals of statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the...
Chapter 3-Learning in Parametric Modeling Basic Concepts and Directions Chapter 4-Mean-Square Error Linear Estimation Chapter 5-Online Learning the Stochastic Gradient Descent Family ofAlgorithms Chapter 6-The Least-Squares Family Chapter 7-Classification a Tour of the Classics ...
Bayesian linear regression solves the problem of overfitting in maximum likelihood estimation. Moreover, it makes full use of data samples and is suitable for modeling complex data [18,19]. In addition to regression, Bayesian reasoning can also be applied in other fields. Some researchers have ...
In this paper, we merge features of the deep Bayesian learning framework with deep kernel learning to leverage the strengths of both methods for a more comprehensive uncertainty estimation. Through a series of experiments on predicting the first incidence of heart failure, diabetes and depression ...