717 research outputs found
Generalized Bayesian MARS: Tools for Emulating Stochastic Computer Models
The multivariate adaptive regression spline (MARS) approach of Friedman
(1991) and its Bayesian counterpart (Francom et al. 2018) are effective
approaches for the emulation of computer models. The traditional assumption of
Gaussian errors limits the usefulness of MARS, and many popular alternatives,
when dealing with stochastic computer models. We propose a generalized Bayesian
MARS (GBMARS) framework which admits the broad class of generalized hyperbolic
distributions as the induced likelihood function. This allows us to develop
tools for the emulation of stochastic simulators which are parsimonious,
scalable, interpretable and require minimal tuning, while providing powerful
predictive and uncertainty quantification capabilities. GBMARS is capable of
robust regression with t distributions, quantile regression with asymmetric
Laplace distributions and a general form of "Normal-Wald" regression in which
the shape of the error distribution and the structure of the mean function are
learned simultaneously. We demonstrate the effectiveness of GBMARS on various
stochastic computer models and we show that it compares favorably to several
popular alternatives
Pseudo Label Selection is a Decision Problem
Pseudo-Labeling is a simple and effective approach to semi-supervised
learning. It requires criteria that guide the selection of pseudo-labeled data.
The latter have been shown to crucially affect pseudo-labeling's generalization
performance. Several such criteria exist and were proven to work reasonably
well in practice. However, their performance often depends on the initial model
fit on labeled data. Early overfitting can be propagated to the final model by
choosing instances with overconfident but wrong predictions, often called
confirmation bias. In two recent works, we demonstrate that pseudo-label
selection (PLS) can be naturally embedded into decision theory. This paves the
way for BPLS, a Bayesian framework for PLS that mitigates the issue of
confirmation bias. At its heart is a novel selection criterion: an analytical
approximation of the posterior predictive of pseudo-samples and labeled data.
We derive this selection criterion by proving Bayes-optimality of this "pseudo
posterior predictive". We empirically assess BPLS for generalized linear,
non-parametric generalized additive models and Bayesian neural networks on
simulated and real-world data. When faced with data prone to overfitting and
thus a high chance of confirmation bias, BPLS outperforms traditional PLS
methods. The decision-theoretic embedding further allows us to render PLS more
robust towards the involved modeling assumptions. To achieve this goal, we
introduce a multi-objective utility function. We demonstrate that the latter
can be constructed to account for different sources of uncertainty and explore
three examples: model selection, accumulation of errors and covariate shift.Comment: Accepted for presentation at the 46th German Conference on Artificial
Intelligenc
Bayesian Inference in Processing Experimental Data: Principles and Basic Applications
This report introduces general ideas and some basic methods of the Bayesian
probability theory applied to physics measurements. Our aim is to make the
reader familiar, through examples rather than rigorous formalism, with concepts
such as: model comparison (including the automatic Ockham's Razor filter
provided by the Bayesian approach); parametric inference; quantification of the
uncertainty about the value of physical quantities, also taking into account
systematic effects; role of marginalization; posterior characterization;
predictive distributions; hierarchical modelling and hyperparameters; Gaussian
approximation of the posterior and recovery of conventional methods, especially
maximum likelihood and chi-square fits under well defined conditions; conjugate
priors, transformation invariance and maximum entropy motivated priors; Monte
Carlo estimates of expectation, including a short introduction to Markov Chain
Monte Carlo methods.Comment: 40 pages, 2 figures, invited paper for Reports on Progress in Physic
Ensemble learning method for hidden markov models.
For complex classification systems, data are gathered from various sources and potentially have different representations. Thus, data may have large intra-class variations. In fact, modeling each data class with a single model might lead to poor generalization. The classification error can be more severe for temporal data where each sample is represented by a sequence of observations. Thus, there is a need for building a classification system that takes into account the variations within each class in the data. This dissertation introduces an ensemble learning method for temporal data that uses a mixture of Hidden Markov Model (HMM) classifiers. We hypothesize that the data are generated by K models, each of which reacts a particular trend in the data. Model identification could be achieved through clustering in the feature space or in the parameters space. However, this approach is inappropriate in the context of sequential data. The proposed approach is based on clustering in the log-likelihood space, and has two main steps. First, one HMM is fit to each of the N individual sequences. For each fitted model, we evaluate the log-likelihood of each sequence. This will result in an N-by-N log-likelihood distance matrix that will be partitioned into K groups using a relational clustering algorithm. In the second step, we learn the parameters of one HMM per group. We propose using and optimizing various training approaches for the different K groups depending on their size and homogeneity. In particular, we investigate the maximum likelihood (ML), the minimum classification error (MCE) based discriminative, and the Variational Bayesian (VB) training approaches. Finally, to test a new sequence, its likelihood is computed in all the models and a final confidence value is assigned by combining the multiple models outputs using a decision level fusion method such as an artificial neural network or a hierarchical mixture of experts. Our approach was evaluated on two real-world applications: (1) identification of Cardio-Pulmonary Resuscitation (CPR) scenes in video simulating medical crises; and (2) landmine detection using Ground Penetrating Radar (GPR). Results on both applications show that the proposed method can identify meaningful and coherent HMM mixture components that describe different properties of the data. Each HMM mixture component models a group of data that share common attributes. The results indicate that the proposed method outperforms the baseline HMM that uses one model for each class in the data
On the usefulness of imprecise Bayesianism in chemical kinetics
International audienceBayesian methods are growing ever more popular in chemical kinetics. The reasons for this and general challenges related to kinetic parameter estimation are shortly reviewed. Most authors content themselves with using one single (mostly uniform) prior distribution. The goal of this paper is to go into some serious issues this raises. The problems of confusing knowledge and ignorance and of reparametrisation are examined. The legitimacy of a probabilistic Ockham’s razor is called into question. A synthetic example involving two reaction models was used to illustrate how merging the parameter space volume with the model accuracy into a single number might be unwise. Robust Bayesian analysis appears to be a simple andstraightforward way to avoid the problems mentioned throughout this article
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