Search results for " Inference"
showing 10 items of 337 documents
Can visualization alleviate dichotomous thinking? Effects of visual representations on the cliff effect
2021
Common reporting styles for statistical results in scientific articles, such as $p$ p -values and confidence intervals (CI), have been reported to be prone to dichotomous interpretations, especially with respect to the null hypothesis significance testing framework. For example when the $p$ p -value is small enough or the CIs of the mean effects of a studied drug and a placebo are not overlapping, scientists tend to claim significant differences while often disregarding the magnitudes and absolute differences in the effect sizes. This type of reasoning has been shown to be potentially harmful to science. Techniques relying on the visual estimation of the strength of evidence have been recom…
Quantum inductive inference by finite automata
2008
AbstractFreivalds and Smith [R. Freivalds, C.H. Smith Memory limited inductive inference machines, Springer Lecture Notes in Computer Science 621 (1992) 19–29] proved that probabilistic limited memory inductive inference machines can learn with probability 1 certain classes of total recursive functions, which cannot be learned by deterministic limited memory inductive inference machines. We introduce quantum limited memory inductive inference machines as quantum finite automata acting as inductive inference machines. These machines, we show, can learn classes of total recursive functions not learnable by any deterministic, nor even by probabilistic, limited memory inductive inference machin…
A genetic integrated fuzzy classifier
2005
This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells. The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good performance and robustness of the integrated classifier strategies.
Improved Frequentist Prediction Intervals for Autoregressive Models by Simulation
2015
It is well known that the so called plug-in prediction intervals for autoregressive processes, with Gaussian disturbances, are too narrow, i.e. the coverage probabilities fall below the nominal ones. However, simulation experiments show that the formulas borrowed from the ordinary linear regression theory yield one-step prediction intervals, which have coverage probabilities very close to what is claimed. From a Bayesian point of view the resulting intervals are posterior predictive intervals when uniform priors are assumed for both autoregressive coefficients and logarithm of the disturbance variance. This finding opens the path how to treat multi-step prediction intervals which are obtain…
On the relative sizes of learnable sets
1998
Abstract Measure and category (or rather, their recursion-theoretical counterparts) have been used in theoretical computer science to make precise the intuitive notion “for most of the recursive sets”. We use the notions of effective measure and category to discuss the relative sizes of inferrible sets, and their complements. We find that inferable sets become large rather quickly in the standard hierarchies of learnability. On the other hand, the complements of the learnable sets are all large.
Weighted-average least squares estimation of generalized linear models
2018
The weighted-average least squares (WALS) approach, introduced by Magnus et al. (2010) in the context of Gaussian linear models, has been shown to enjoy important advantages over other strictly Bayesian and strictly frequentist model averaging estimators when accounting for problems of uncertainty in the choice of the regressors. In this paper we extend the WALS approach to deal with uncertainty about the specification of the linear predictor in the wider class of generalized linear models (GLMs). We study the large-sample properties of the WALS estimator for GLMs under a local misspecification framework that allows the development of asymptotic model averaging theory. We also investigate t…
A Phylogenetic Analysis of Human Syntenies Revealed by Chromosome Painting in Euarchontoglires Orders
2010
To search for cytogenetic signatures that can help to clarify evolutionary affinities among the five orders within the Euarchontoglires clade, we focused on associations of conserved syntenic blocks that have been accumulated in the karyotypes of Primates (Strepsirhini and Haplorhini), five families of Rodentia, Scandentia (Tupaia belangeri), Dermoptera (Galeopterus variegatus) and Lagomorpha (Oryctolagus cuniculus). We examined available chromosome painting data to identify conserved chromosomes and chromosomal segments, and syntenic associations likely to have characterized the ancestral eutherian karyotype. The data set includes 161 characters that have been subjected to a concatenated a…
Selective phenotyping, entropy reduction, and the mastermind game.
2011
Abstract Background With the advance of genome sequencing technologies, phenotyping, rather than genotyping, is becoming the most expensive task when mapping genetic traits. The need for efficient selective phenotyping strategies, i.e. methods to select a subset of genotyped individuals for phenotyping, therefore increases. Current methods have focused either on improving the detection of causative genetic variants or their precise genomic location separately. Results Here we recognize selective phenotyping as a Bayesian model discrimination problem and introduce SPARE (Selective Phenotyping Approach by Reduction of Entropy). Unlike previous methods, SPARE can integrate the information of p…
Bayesian Survival Analysis to Model Plant Resistance and Tolerance to Virus Diseases
2017
Viruses constitute a major threat to large-scale production of crops worldwide producing important economical losses and undermining sustainability. We evaluated a new plant variety for resistance and tolerance to a specific virus through a comparison with other well-known varieties. The study is based on two independent Bayesian accelerated failure time models which assess resistance and tolerance survival times. Information concerning plant genotype and virus biotype were considered as baseline covariates and error terms were assumed to follow a modified standard Gumbel distribution. Frequentist approach to these models was also considered in order to compare the results of the study from…
Modelling the presence of disease under spatial misalignment using Bayesian latent Gaussian models.
2015
Modelling patterns of the spatial incidence of diseases using local environmental factors has been a growing problem in the last few years. Geostatistical models have become popular lately because they allow estimating and predicting the underlying disease risk and relating it with possible risk factors. Our approach to these models is based on the fact that the presence/absence of a disease can be expressed with a hierarchical Bayesian spatial model that incorporates the information provided by the geographical and environmental characteristics of the region of interest. Nevertheless, our main interest here is to tackle the misalignment problem arising when information about possible covar…