Search results for "Bayesian"
showing 10 items of 604 documents
What should I do next? Using shared representations to solve interaction problems
2011
Studies on how “the social mind” works reveal that cognitive agents engaged in joint actions actively estimate and influence another’s cognitive variables and form shared representations with them. (How) do shared representations enhance coordination? In this paper, we provide a probabilistic model of joint action that emphasizes how shared representations help solving interaction problems. We focus on two aspects of the model. First, we discuss how shared representations permit to coordinate at the level of cognitive variables (beliefs, intentions, and actions) and determine a coherent unfolding of action execution and predictive processes in the brains of two agents. Second, we discuss th…
Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers
2016
In this paper, we present a novel algorithm that performs online histogram-based classification, i.e., specifically designed for the case when the data is dynamic and its distribution is non-stationary. Our method, called the Online Histogram-based Naïve Bayes Classifier (OHNBC) involves a statistical classifier based on the well-established Bayesian theory, but which makes some assumptions with respect to the independence of the attributes. Moreover, this classifier generates a prediction model using uni-dimensional histograms, whose segments or buckets are fixed in terms of their cardinalities but dynamic in terms of their widths. Additionally, our algorithm invokes the principles of info…
Online Estimation of Discrete Densities
2013
We address the problem of estimating a discrete joint density online, that is, the algorithm is only provided the current example and its current estimate. The proposed online estimator of discrete densities, EDDO (Estimation of Discrete Densities Online), uses classifier chains to model dependencies among features. Each classifier in the chain estimates the probability of one particular feature. Because a single chain may not provide a reliable estimate, we also consider ensembles of classifier chains and ensembles of weighted classifier chains. For all density estimators, we provide consistency proofs and propose algorithms to perform certain inference tasks. The empirical evaluation of t…
Experiments with an adaptive Bayesian restoration method
1989
Abstract This paper describes a Bayesian restoration method applied to two-dimensional measured images, whose detector response function is not completely known. The response function is assumed Gaussian with standard deviation depending on the estimate of the local density of the image. The convex hull of the K -nearest neighbours ( K NN) of each ‘on’ pixel is used to compute the local density. The method has been tested on ‘sparse’ images, with and without noise background.
Correlation and Truth
2013
The concept of correlation is the building block of almost any Bayesian attempt to capture or explicate any interesting aspect of scientific reasoning in terms of probabilities. This paper discusses one particularly simple correlation measure which is highly significant for almost any such attempt within the philosophy of science or epistemology. In particular, it shows how this correlation measure is related to central attempts to capture essential aspects of scientific reasoning such as confirmation, coherence, and the explanatory power of hypotheses. This intimate connection between correlation and scientific reasoning necessitates answering the question of how correlation and truth are …
A Fatty Acid Based Bayesian Approach for Inferring Diet in Aquatic Consumers
2015
We modified the stable isotope mixing model MixSIR to infer primary producer contributions to consumer diets based on their fatty acid composition. To parameterize the algorithm, we generated a 'consumer-resource library' of FA signatures of Daphnia fed different algal diets, using 34 feeding trials representing diverse phytoplankton lineages. This library corresponds to the resource or producer file in classic Bayesian mixing models such as MixSIR or SIAR. Because this library is based on the FA profiles of zooplankton consuming known diets, and not the FA profiles of algae directly, trophic modification of consumer lipids is directly accounted for. To test the model, we simulated hypothet…
Network reconstruction for trans acting genetic loci using multi-omics data and prior information.
2022
Background: Molecular measurements of the genome, the transcriptome, and the epigenome, often termed multi-omics data, provide an in-depth view on biological systems and their integration is crucial for gaining insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans-QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information can improve network inference. However, previous efforts were limited in the types of priors…
Data Augmentation Approach in Bayesian Modelling of Presence-only Data
2011
Abstract Ecologists are interested in prediction of potential distribution of species in suitable areas, essential for planning conservation and management strategies. Unfortunately, often the only available information in such studies is the true presence of the species at few locations of the study area and the associated environmental covariates over the entire area, referred as presence-only data. We propose a Bayesian approach to estimate logistic linear regressions adapted to presence-only data through the introduction of a random approximation of the correction factor in the adjusted logistic model that allows us to overcome the need to know a priori the prevalence of the species.
Comparing normal means: new methods for an old problem
2007
Comparing the means of two normal populations is an old problem in mathematical statistics, but there is still no consensus about its most appropriate solution. In this paper we treat the problem of comparing two normal means as a Bayesian decision problem with only two alternatives: either to accept the hypothesis that the two means are equal, or to conclude that the observed data are, under the assumed model, incompatible with that hypothesis. The combined use of an information-theory based loss function, the intrinsic discrepancy (Bernardo and Rueda 2002}, and an objective prior function, the reference prior \citep{Bernardo 1979; Berger and Bernardo 1992), produces a new solution to this…
Early stages of the acute physical stress response increase loss aversion and learning on decision making: A Bayesian approach
2021
Abstract When the cortisol peak is reached after a stressor people learn slower and make worse decisions in the Iowa Gambling Task (IGT). However, the effects of the early stress response have not received as much attention. Since physical exercise is an important neuroendocrine stressor, this study aimed to fill this gap using an acute physical stressor. We hypothesized that this stress stage would promote an alertness that may increase feedback-sensitivity and, therefore, reward-learning during IGT, leading to a greater overall decision-making. 90 participants were divided into two groups: 47 were exposed to an acute intense physical stressor (cycloergometer) and 43 to a distractor 5 min …