David Spiegelhalter: The Art of Statistics: Learning from Data

Society ◽  
2021 ◽  
Author(s):  
Michael Laver
Keyword(s):  
2010 ◽  
Vol 110 (23) ◽  
pp. 1031-1036 ◽  
Author(s):  
Giorgio Gnecco ◽  
Marcello Sanguineti

2019 ◽  
Author(s):  
Jodyn E Platt ◽  
Minakshi Raj ◽  
Matthias Wienroth

BACKGROUND In the past decade, Lynn Etheredge presented a vision for the Learning Health System (LHS) as an opportunity for increasing the value of health care via rapid learning from data and immediate translation to practice and policy. An LHS is defined in the literature as a system that seeks to continuously generate and apply evidence, innovation, quality, and value in health care. OBJECTIVE This review aimed to examine themes in the literature and rhetoric on the LHS in the past decade to understand efforts to realize the LHS in practice and to identify gaps and opportunities to continue to take the LHS forward. METHODS We conducted a thematic analysis in 2018 to analyze progress and opportunities over time as compared with the initial <i>Knowledge Gaps and Uncertainties</i> proposed in 2007. RESULTS We found that the literature on the LHS has increased over the past decade, with most articles focused on theory and implementation; articles have been increasingly concerned with policy. CONCLUSIONS There is a need for attention to understanding the ethical and social implications of the LHS and for exploring opportunities to ensure that these implications are salient in implementation, practice, and policy efforts.


Author(s):  
Shervan Gharari ◽  
Hoshin V. Gupta ◽  
Martyn P. Clark ◽  
Markus Hrachowitz ◽  
Fabrizio Fenicia ◽  
...  

2015 ◽  
Vol 24 (04) ◽  
pp. 1550012
Author(s):  
Yanying Li ◽  
Youlong Yang ◽  
Wensheng Wang ◽  
Wenming Yang

It is well known that Bayesian network structure learning from data is an NP-hard problem. Learning a correct skeleton of a DAG is the foundation of dependency analysis algorithms for this problem. Considering the unreliability of the high order condition independence (CI) tests and the aim to improve the efficiency of a dependency analysis algorithm, the key steps are to use less number of CI tests and reduce the sizes of condition sets as many as possible. Based on these analyses and inspired by the algorithm HPC, we present an algorithm, named efficient hybrid parents and child (EHPC), for learning the adjacent neighbors of every variable. We proof the validity of the algorithm. Compared with state-of-the-art algorithms, the experimental results show that EHPC can handle large network and has better accuracy with fewer number of condition independence tests and smaller size of conditioning set.


Author(s):  
Lala Septem Riza ◽  
Christoph Bergmeir ◽  
Francisco Herrera ◽  
Jose Manuel Benitez
Keyword(s):  

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