Pages that link to "Item:Q1766320"

The following pages link to Bayesian methods for neural networks and related models (Q1766320):

Displaying 43 items.

• On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks (Q280238) (← links)
• Invariance priors for Bayesian feed-forward neural networks (Q858888) (← links)
• Variational approximations in Bayesian model selection for finite mixture distributions (Q1020214) (← links)
• A noninformative prior for neural networks (Q1395746) (← links)
• Computational advances for and from Bayesian analysis (Q1766319) (← links)
• Approximate algorithms for neural-Bayesian approaches. (Q1853467) (← links)
• Bayesian learning for neural networks (Q1922287) (← links)
• A program for the Bayesian neural network in the ROOT framework (Q1943070) (← links)
• Functional regression via variational Bayes (Q1952199) (← links)
• A variational inference for the Lévy adaptive regression with multiple kernels (Q2095764) (← links)
• Challenges in Markov chain Monte Carlo for Bayesian neural networks (Q2163079) (← links)
• Quantile regression neural networks: a Bayesian approach (Q2241709) (← links)
• Gradient conjugate priors and multi-layer neural networks (Q2289027) (← links)
• On the relative expressiveness of Bayesian and neural networks (Q2302782) (← links)
• A variational Bayesian approach for inverse problems with skew-\(t\) error distributions (Q2374776) (← links)
• Default priors for neural network classification (Q2475997) (← links)
• Robust full Bayesian learning for radial basis networks (Q2762431) (← links)
• Bayesian inference in neural networks (Q2774522) (← links)
• Bayesian MLP neural networks for image analysis (Q2783100) (← links)
• Semiparametric regression and graphical models (Q2802725) (← links)
• Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (with discussion) (Q2920273) (← links)
• Gaussian Scale Mixture Models for Robust Linear Multivariate Regression with Missing Data (Q3178490) (← links)
• Streamlined mean field variational Bayes for longitudinal and multilevel data analysis (Q3188702) (← links)
• Variational Bayes Solution of Linear Neural Networks and Its Generalization Performance (Q3440433) (← links)
• Statistical challenges of high-dimensional data (Q3559944) (← links)
• Modeling Motor Learning Using Heteroscedastic Functional Principal Components Analysis (Q4559684) (← links)
• Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network (Q4601242) (← links)
• (Q4829121) (← links)
• Multiscale Bayesian state-space model for Granger causality analysis of brain signal (Q5036486) (← links)
• (Q5053275) (← links)
• Bayesian regularized artificial neural networks for the estimation of the probability of default (Q5121501) (← links)
• (Q5214290) (← links)
• Algorithmic Learning Theory (Q5464519) (← links)
• Advances in Neural Networks – ISNN 2005 (Q5706959) (← links)
• Empirical Bayes method for Boltzmann machines (Q5870270) (← links)
• Neural network modelling with input uncertainty: Theory and application (Q5926457) (← links)
• Designing neural networks that process mean values of random variables (Q5964310) (← links)
• Approximate blocked Gibbs sampling for Bayesian neural networks (Q6063143) (← links)
• Laplace based Bayesian inference for ordinary differential equation models using regularized artificial neural networks (Q6063150) (← links)
• Gradient and uncertainty enhanced sequential sampling for global fit (Q6096471) (← links)
• Concentration of measure and global optimization of Bayesian multilayer perceptron. I (Q6586789) (← links)
• A deep learning -- genetic algorithm approach for aerodynamic inverse design via optimization of pressure distribution (Q6588345) (← links)
• Variational estimation for multidimensional generalized partial credit model (Q6657614) (← links)