On a Measure of the Information Provided by an Experiment

By D. Lindley. 1956

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Citations in the corpus

Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, background): “, how much information this query will provide as in [98].”
Bayesian Active Learning for Classification and Preference Learning (2011, methodology): “Although the latter approach was proposed several decades ago [Lindley, 1956, Bernardo, 1979], it is not always straightforward to apply the criteria to complicated models such as nonparametric processes with inﬁnite parameter spaces.”
Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies (2008, unknown): “Maximizing mutual information between sets of random variables has a long history of use in statistics (Lindley, 1956; Bernardo, 1979), machine learning (Luttrell, 1985; MacKay, 1992).”
Model Selection and the Principle of Minimum Description Length (2001, methodology): “Θ and has been used to measure information in a statistical context by Lindley as early as 1956 (cf. Lindley, 1956 ).”
Maximum entropy sampling and optimal Bayesian experimental design (2000, background): “De®nitive papers are Lindley (1956), Stone (1959) and Renyi (1961), and an elegant summary appears in Renyi (1970), chapter 9.”
The Design and Analysis of Computer Experiments (2003, background): “A criterion advanced by Lindley (1956) in his work on Bayesian design is the minimization of the expected posterior entropy.”
Bayesian Workflow (2020, methodology): “If our fake-data check fails, in the sense that the inferences are not close to the assumed parameter values or if there seem to be model components that are not gaining any information from the data (Lindley, 1956, Goel and DeGroot, 1981), we recommend breaking down the model.”
Planning to Explore via Self-Supervised World Models (2020, methodology): “This is formalized in the expected information gain (Lindley, 1956), which we approximate as the disagreement in predictions of an ensemble of one-step models.”
On the measure of the information in a statistical experiment (2007, methodology): “Following the lead by Lindley (1956, 1972), Good (1960) and Rényi (1967a,b), (36) has become the default design optimality criteria in Bayesian DoE.”
Selective Sampling Using the Query by Committee Algorithm (1997, background): “It is simple algebra to check that the following choice of m0 and satisfies Equations (9) and ( 14 ):”
Précis of Bayesian Rationality: The Probabilistic Approach to Human Reasoning (2009, methodology): “) Better-motivated theoretical models of the value of information, such as information gain-KL distance (Lindley 1956; Oaksford & Chater 1994), probability gain (error reduction; cf.”