Results 141 to 150 of about 6,917,983 (390)
Margins of discrete Bayesian networks [PDF]
The Annals of Statistics, 2018 41 ...
openaire +6 more sources
Advanced Intelligent Systems, EarlyView.In this article, an energy‐efficient hardware implementation of spiking‐restricted Boltzmann machines using the pseudo‐synaptic sampling (PS2) method is presented. In the PS2 method, superior area and energy efficiency over previous approaches, such as the random walk method, are demonstrated, achieving a 94.94% reduction in power consumption during on‐
Hyunwoo Kim +10 more
wiley +1 more source
Energy constraint Bayesian compressive sensing detection algorithm
Tongxin xuebao, 2012 To solve the shortage of nodes handling ability and limited energy in wireless sensor network,an energy constraint Bayesian compressive sensing detection algorithm was proposed.To balance the energy of the whole network and prevent network paralyzed due ...
Chun-hui ZHAO, Yun-long XU
doaj +2 more sources
Bayesian network considering the clustering of the customers in a hair salon
Cogent Business & Management, 2019 The service industry, which includes hair salons, currently accounts for almost 70% of Japan’s GDP(Gross Domestic Product). Although hair salons are frequently used, over the years, the industry has decreased in size.
Yuki Horita, Haruka Yamashita
doaj +1 more source
Are Bayesian networks typically faithful? [PDF]
arXivFaithfulness is a ubiquitous assumption in causal inference, often motivated by the fact that the faithful parameters of linear Gaussian and discrete Bayesian networks are typical, and the folklore belief that this should also hold for other classes of Bayesian networks.
arxiv
Learning and Testing Causal Models with Interventions [PDF]
arXiv, 2018 We consider testing and learning problems on causal Bayesian networks as defined by Pearl (Pearl, 2009). Given a causal Bayesian network $\mathcal{M}$ on a graph with $n$ discrete variables and bounded in-degree and bounded `confounded components', we show that $O(\log n)$ interventions on an unknown causal Bayesian network $\mathcal{X}$ on the same ...
arxiv
Automated construction of sparse Bayesian networks from unstructured probabilistic models and domain information [PDF]
, 1990 Sampath Srinivas +2 more
openalex +1 more source
Deep Learning Methods in Soft Robotics: Architectures and Applications
Advanced Intelligent Systems, EarlyView.Soft robotics has seen intense research over the past two decades and offers a promising approach for future robotic applications. However, standard industrial methods may be challenging to apply to soft robots. Recent advances in deep learning provide powerful tools to analyze and design complex soft machines that can operate in unstructured ...
Tomáš Čakurda +3 more
wiley +1 more source
A Note on Bayesian Networks with Latent Root Variables [PDF]
arXivWe characterise the likelihood function computed from a Bayesian network with latent variables as root nodes. We show that the marginal distribution over the remaining, manifest, variables also factorises as a Bayesian network, which we call empirical.
arxiv
Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structure [PDF]
arXiv, 2019 Bayesian neural networks (BNNs) augment deep networks with uncertainty quantification by Bayesian treatment of the network weights. However, such models face the challenge of Bayesian inference in a high-dimensional and usually over-parameterized space.
arxiv