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Objective Bayes and Conditional Frequentist Inference [PDF]
, 2011 Objective Bayesian methods have garnered considerable interest and support among statisticians, particularly over the past two decades. It has often been ignored, however, that in some cases the appropriate frequentist inference to match is a ...
Kuffner, Todd Alan, Kuffner, Todd Alan
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Conditional Probability and Defeasible Inference [PDF]
Journal of Philosophical Logic, 2005 We offer a probabilistic model of rational consequence relations (Lehmann and Magidor, 1990) by appealing to the extension of the classical Ramsey–Adams test proposed by Vann McGee in (McGee, 1994). Previous and influential models of non-monotonic consequence relations have been produced in terms of the dynamics of expectations (Gärdenfors and Makinson,
Arló Costa, Horacio, Parikh, Rohit
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Conditionals As Representative Inferences [PDF]
Axiomathes, 2020 AbstractAccording to Adams (Inquiry 8:166–197, 1965), the acceptability of an indicative conditional goes with the conditional probability of the consequent given the antecedent. However, some conditionals seem to be inappropriate, although their corresponding conditional probability is high.
Robert van Rooij, Katrin Schulz
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Randomization Tests that Condition on Non-Categorical Covariate Balance
Journal of Causal Inference, 2019 A benefit of randomized experiments is that covariate distributions of treatment and control groups are balanced on average, resulting in simple unbiased estimators for treatment effects.
Branson Zach, Miratrix Luke W.
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Conditional Deep Gaussian Processes: Empirical Bayes Hyperdata Learning
Entropy, 2021 It is desirable to combine the expressive power of deep learning with Gaussian Process (GP) in one expressive Bayesian learning model. Deep kernel learning showed success as a deep network used for feature extraction.
Chi-Ken Lu, Patrick Shafto
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“Inference versus consequence” revisited: inference, consequence, conditional, implication [PDF]
Synthese, 2011 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Model Free Inference on Multivariate Time Series with Conditional Correlations
Stats, 2020 New results on volatility modeling and forecasting are presented based on the NoVaS transformation approach. Our main contribution is that we extend the NoVaS methodology to modeling and forecasting conditional correlation, thus allowing NoVaS to work in
Dimitrios Thomakos +2 more
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BMC Medical Research Methodology, 2022 Background Variable selection for regression models plays a key role in the analysis of biomedical data. However, inference after selection is not covered by classical statistical frequentist theory, which assumes a fixed set of covariates in the model ...
Michael Kammer +3 more
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Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models
, 2017 This paper studies inference of preference parameters in semiparametric discrete choice models when these parameters are not point-identified and the identified set is characterized by a class of conditional moment inequalities.
Chen, Le-Yu, Lee, Sokbae
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Brain Imaging, Forward Inference, and Theories of Reasoning
Frontiers in Human Neuroscience, 2015 This review focuses on the issue of how neuroimaging studies address theoretical accounts of reasoning, through the lens of the method of forward inference (Henson, 2005, 2006).
Evan eHeit
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