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Last Layer Marginal Likelihood for Invariance Learning [PDF]
, 2022 Data augmentation is often used to incorporate inductive biases into models. Traditionally, these are hand-crafted and tuned with cross validation. The Bayesian paradigm for model selection provides a path towards end-to-end learning of invariances using
Jørgensen, Martin +3 more
core
Gaussian Copula Regression in R
Journal of Statistical Software, 2017 This article describes the R package gcmr for fitting Gaussian copula marginal regression models. The Gaussian copula provides a mathematically convenient framework to handle various forms of dependence in regression models arising, for example, in time ...
Guido Masarotto, Cristiano Varin
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Effect of Dependency on the Estimation of P[Y
Austrian Journal of Statistics, 2022 We consider an expression for the probability R=P ...
Dipak Patil +2 more
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StatsThe two-parameter normal-ogive (2PNO) model is one of the most popular item response theory (IRT) models for analyzing dichotomous items. Consistent parameter estimation of the 2PNO model using marginal maximum likelihood estimation relies on the local ...
Alexander Robitzsch
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Marginal Likelihoods in Phylogenetics: A Review of Methods and Applications
Systematic Biology, 2019 AbstractBy providing a framework of accounting for the shared ancestry inherent to all life, phylogenetics is becoming the statistical foundation of biology. The importance of model choice continues to grow as phylogenetic models continue to increase in complexity to better capture micro- and macroevolutionary processes.
Oaks, Jamie R +3 more
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Marginal Likelihood Estimation with the Cross-Entropy Method [PDF]
Econometric Reviews, 2012 We consider an adaptive importance sampling approach to estimating the marginal likelihood, a quantity that is fundamental in Bayesian model comparison and Bayesian model averaging. This approach is motivated by the difficulty of obtaining an accurate estimate through existing algorithms that use Markov chain Monte Carlo (MCMC) draws, where the draws ...
Chan, Chi Chun (Joshua), Eisenstat, Eric
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Recursive pathways to marginal likelihood estimation with prior-sensitivity analysis [PDF]
, 2014 We investigate the utility to computational Bayesian analyses of a particular family of recursive marginal likelihood estimators characterized by the (equivalent) algorithms known as "biased sampling" or "reverse logistic regression" in the statistics ...
Cameron, Ewan, Pettitt, Anthony N.
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AlgorithmsWe consider the estimation of the marginal likelihood in Bayesian statistics, with primary emphasis on Gaussian graphical models, where the intractability of the marginal likelihood in high dimensions is a frequently researched problem.
Eric Chuu +3 more
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FEBS Letters, EarlyView.We reconstituted Synechocystis glycogen synthesis in vitro from purified enzymes and showed that two GlgA isoenzymes produce glycogen with different architectures: GlgA1 yields denser, highly branched glycogen, whereas GlgA2 synthesizes longer, less‐branched chains.
Kenric Lee +3 more
wiley +1 more source
Impacts of prior parameter distributions on Bayesian evaluation of groundwater model complexity
Water Science and Engineering, 2018 This study used the marginal likelihood and Bayesian posterior model probability for evaluation of model complexity in order to avoid using over-complex models for numerical simulations.
Saeideh Samani +6 more
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