Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics
Building on a strong foundation of philosophy, theory, methods and computation over the
past three decades, Bayesian approaches are now an integral part of the toolkit for most …
past three decades, Bayesian approaches are now an integral part of the toolkit for most …
Bayesian statistics for medical devices: progress since 2010
G Campbell, T Irony, G Pennello… - Therapeutic Innovation & …, 2023 - Springer
The use of Bayesian statistics to support regulatory evaluation of medical devices began in
the late 1990s. We review the literature, focusing on recent developments of Bayesian …
the late 1990s. We review the literature, focusing on recent developments of Bayesian …
[HTML][HTML] A hybrid autoformer framework for electricity demand forecasting
Electricity demand forecasting is of great significance to the electricity system and residents'
life, but it is difficult to forecast the electricity demand series because of the influence of …
life, but it is difficult to forecast the electricity demand series because of the influence of …
A study on the power parameter in power prior Bayesian analysis
The power prior and its variations have been proven to be a useful class of informative priors
in Bayesian inference due to their flexibility in incorporating the historical information by …
in Bayesian inference due to their flexibility in incorporating the historical information by …
Using Bayesian statistics in confirmatory clinical trials in the regulatory setting: a tutorial review
SY Lee - BMC Medical Research Methodology, 2024 - Springer
Bayesian statistics plays a pivotal role in advancing medical science by enabling healthcare
companies, regulators, and stakeholders to assess the safety and efficacy of new treatments …
companies, regulators, and stakeholders to assess the safety and efficacy of new treatments …
Optimal priors for the discounting parameter of the normalized power prior
The power prior is a popular class of informative priors for incorporating information from
historical data. It involves raising the likelihood for the historical data to a power, which acts …
historical data. It involves raising the likelihood for the historical data to a power, which acts …
Exploring the Connection Between the Normalized Power Prior and Bayesian Hierarchical Models
The power prior is a popular class of informative priors for incorporating information from
historical data. It involves raising the likelihood for the historical data to a power, which acts …
historical data. It involves raising the likelihood for the historical data to a power, which acts …
Study on apple damage detecting method based on relaxation single-wavelength laser and convolutional neural network
J Ning, H Ye, Y Sun, J Zhang, Z Mei, S Xiong… - Journal of Food …, 2022 - Springer
Apple is usually damaged by mechanical factors, including harvesting methods and
transportation. The mechanical damage to apples induces great economic loss every year …
transportation. The mechanical damage to apples induces great economic loss every year …
Comparing methods for determining power priors based on different congruence measures
Experiments are routinely conducted to evaluate the toxicity of water effluents or chemicals.
In a Ceriodaphnia dubia reproduction test, organisms are exposed to varying concentration …
In a Ceriodaphnia dubia reproduction test, organisms are exposed to varying concentration …
A scaled kernel density estimation prior for dynamic borrowing of historical information with application to clinical trial design
JL Warren, Q Wang, MM Ciarleglio - Statistics in Medicine, 2024 - Wiley Online Library
Incorporating historical data into a current data analysis can improve estimation of
parameters shared across both datasets and increase the power to detect associations of …
parameters shared across both datasets and increase the power to detect associations of …