This tutorial component includes defining priors based on literature and performing a sensitivity analysis to test for potential misspecification or bias. For readers new to Bayesian statistics, the ...

2 College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China Objective To demonstrate an application of Bayesian model averaging (BMA) with generalised additive ...

Overall, 71.4% of studies reported non-significant primary outcomes, with an increasing trend observed over time. Conclusion: Bayesian analysis offers a useful framework for interpreting "negative ...

Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...

Find out more about undergraduate study at the School of Electronic Engineering and Computer Science.

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of ...

[Notice] This list is not being maintained anymore because of the overwhelming amount of deep learning papers published every day since 2017. A curated list of the most cited deep learning papers ...

Healthcare decisions should be based on all relevant evidence.1 Usually, this is provided by randomised controlled trials (RCTs) comparing two or more interventions for a condition affecting a target ...

Orthogonal Frequency Division Multiplexing,Time Slot,Base Station,Optimization Problem,Wireless Networks,Additive Noise,Channel Estimation,Communication Systems,User ...