Purpose Nature-based tourism (NBT) blossoming requires sound monitoring models to maximize its potential in the industry. Cooperation of different segments from nature economy will lead a sustainable NBT. Therefore, qualitative and quantitative relation between these subdivisions has be investigated. Design/methodology/approach This paper proposes an advanced NBT model for design optimum system...

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 15 June 2020Accepted: 06 August 2021Published online: 01 November 2021Keywordsnonparametric Bayesian approach, design experiments, stochastic simulation, uncertainty quantification, input uncertainty, Dirichlet process mixturesAMS Subject Headings90-10Publication DataISSN...

In this paper, we apply popular Bayesian techniques on support vector classifier. We propose a novel differentiable loss function called trigonometric loss function with the desirable characteristics of natural normalization in the likelihood function, and then follow standard Gaussian processes techniques to set up a Bayesian framework. In this framework, Bayesian inference is used to implemen...

We show how improved sequences for magnetic resonance imaging can be found through optimization of Bayesian design scores. Combining approximate Bayesian inference and natural image statistics with high-performance numerical computation, we propose the first Bayesian experimental design framework for this problem of high relevance to clinical and brain research. Our solution requires large-scal...

Structure learning in Bayesian network is a big issue. Many efforts have tried to solve this problem and quite a few algorithms have been proposed. However, when we attempt to apply the existing methods to microarray data, there are three main challenges: 1) there are many variables in the data set, 2) the sample size is small, and 3) microarray data are changing from experiment to experiment a...

As it strives to imitate observably successful actions, imitation learning allows for a quick acquisition of proven behaviors. Recent work from psychology and robotics suggests that Bayesian probability theory provides a mathematical framework for imitation learning. In this paper, we investigate the use of Bayesian imitation learning in realizing more life-like computer game characters. Follow...

Bayesian optimization is a powerful framework for minimizing expensive objective functions while using very few function evaluations. It has been successfully applied to a variety of problems, including hyperparameter tuning and experimental design. However, this framework has not been extended to the inequality-constrained optimization setting, particularly the setting in which evaluating feas...