Designing efficient Bayesian sampling plans for two-component exponential distributions model
Sign Up to like & getrecommendations! Published in 2025 at "Journal of Statistical Computation and Simulation"
DOI: 10.1080/00949655.2024.2447862
Abstract: This article studies a method about how to design efficient Bayesian sampling plans based on samples collected from two-component exponential distributions. First, based on a complete sampling, for a general loss function, a Bayes decision… read more here.
Keywords: bayesian sampling; efficient bayesian; delta scriptscriptstyle; two component ... See more keywords
Designing efficient Bayesian sampling plans for two-parameter exponential distribution with censored data
Sign Up to like & getrecommendations! Published in 2025 at "Communications in Statistics - Theory and Methods"
DOI: 10.1080/03610926.2025.2496688
Abstract: Abstract. This article studies a method about how to design Bayesian sampling plans for two-parameter exponential distributions E(μ, λ) based on Type-II censored samples. With a linear loss of the expected life time θ=μ+1/λ, a… read more here.
Keywords: two parameter; bayesian sampling; plans two; efficient bayesian ... See more keywords
Energy-Efficient Bayesian Inference Using Bitstream Computing
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Computer Architecture Letters"
DOI: 10.1109/lca.2023.3238584
Abstract: Uncertainty quantification is critical to many machine learning applications especially in mobile and edge computing tasks like self-driving cars, robots, and mobile devices. Bayesian Neural Networks can be used to provide these uncertainty quantifications but… read more here.
Keywords: bitstream; efficient bayesian; energy; bayesian inference ... See more keywords
An Energy-Efficient Bayesian Neural Network Implementation Using Stochastic Computing Method.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2023.3265533
Abstract: The robustness of Bayesian neural networks (BNNs) to real-world uncertainties and incompleteness has led to their application in some safety-critical fields. However, evaluating uncertainty during BNN inference requires repeated sampling and feed-forward computing, making them… read more here.
Keywords: bayesian neural; efficient bayesian; energy; stochastic computing ... See more keywords
Efficient Bayesian inference for stochastic agent-based models
Sign Up to like & getrecommendations! Published in 2022 at "PLoS Computational Biology"
DOI: 10.1371/journal.pcbi.1009508
Abstract: The modelling of many real-world problems relies on computationally heavy simulations of randomly interacting individuals or agents. However, the values of the parameters that underlie the interactions between agents are typically poorly known, and hence… read more here.
Keywords: efficient bayesian; machine learning; bayesian inference; real world ... See more keywords
Efficient Bayesian inference for mechanistic modelling with high-throughput data
Sign Up to like & getrecommendations! Published in 2022 at "PLoS Computational Biology"
DOI: 10.1371/journal.pcbi.1010191
Abstract: Bayesian methods are routinely used to combine experimental data with detailed mathematical models to obtain insights into physical phenomena. However, the computational cost of Bayesian computation with detailed models has been a notorious problem. Moreover,… read more here.
Keywords: efficient bayesian; bayesian inference; high throughput; throughput data ... See more keywords