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A Comparison of Marginal Likelihood Computation Methods [PDF]
SSRN Electronic Journal, 2002 In a Bayesian analysis, different models can be compared on the basis of the expected or marginal likelihood they attain. Many methods have been devised to compute the marginal likelihood, but simplicity is not the strongest point of most methods. At the same time, the precision of methods is often questionable.
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BMC Medical Research Methodology, 2021 Background When quantifying the probability of survival in cancer patients using cancer registration data, it is common to estimate marginal relative survival, which under assumptions can be interpreted as marginal net survival.
Paul C. Lambert +2 more
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Classifier Learning with Supervised Marginal Likelihood
CoRR, 2013 It has been argued that in supervised classification tasks, in practice it may be more sensible to perform model selection with respect to some more focused model selection score, like the supervised (conditional) marginal likelihood, than with respect to the standard marginal likelihood criterion.
Petri Kontkanen +2 more
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Retrieval of Experiments by Efficient Comparison of Marginal Likelihoods [PDF]
, 2014 We study the task of retrieving relevant experiments given a query experiment. By experiment, we mean a collection of measurements from a set of ‘covariates’ and the associated ‘outcomes’. While similar experiments can be retrieved by comparing available ‘annotations’, this approach ignores the valuable information available in the measurements ...
Sohan Seth +2 more
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Warranty Claim Forecasting Based On Weighted Maximum Likelihood Estimation [PDF]
, 2012 Warranty claims reported in recent months might carry more up-to-date information than those reported in earlier months. Using weighted maximum likelihood estimation for estimating model parameters might therefore lead to better performance of warranty ...
Akbarov, Artur +3 more
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Genetics Selection Evolution, 2009 Background Marginal posterior genotype probabilities need to be computed for genetic analyses such as geneticcounseling in humans and selective breeding in animal and plant species. Methods In this paper, we describe a peeling based, deterministic, exact
Fernando Rohan L +2 more
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Learning Invariances using the Marginal Likelihood
CoRR, 2018 Generalising well in supervised learning tasks relies on correctly extrapolating the training data to a large region of the input space. One way to achieve this is to constrain the predictions to be invariant to transformations on the input that are known to be irrelevant (e.g. translation).
Van der Wilk, M. +3 more
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MathematicsEfficient estimation methods for simultaneous autoregressive (SAR) models with missing data in the response variable have been well explored in the literature.
Anjana Wijayawardhana +2 more
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Improved Uncertainty Quantification for Neural Networks With Bayesian Last Layer
IEEE Access, 2023 Uncertainty quantification is an important task in machine learning - a task in which standard neural networks (NNs) have traditionally not excelled. This can be a limitation for safety-critical applications, where uncertainty-aware methods like Gaussian
Felix Fiedler, Sergio Lucia
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On weighting of bivariate margins in pairwise likelihood
Journal of Multivariate Analysis, 2009 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Harry Joe, Youngjo Lee 0001
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