We discuss epistemological and methodological aspects of the Bayesian approach in astrophysics and cosmology. The introduction to the Bayesian framework is given for a further discussion concerning the Bayesian inference in physics. The interplay between the modern cosmology, Bayesian statistics, and philosophy of science is presented. We consider paradoxes of confi rmation, like Goodman’s paradox, appearing in the Bayesian theory of confirmation. As in Goodman’s paradox the Bayesian inference is susceptible to some epistemic limitations in the logic of induction. However, Goodman’s paradox applied to cosmological hypotheses seems to be resolved due to the evolutionary character of cosmology and the accumulation of new empirical evidence. We argue that the Bayesian framework is useful in the context of falsifiability of quantum cosmological models, as well as contemporary dark energy and dark matter problem.

When the distribution of water quality samples is roughly balanced, the Bayesian criterion model of water-inrush source generally can obtain relatively accurate results of water-inrush source identification. However, it is often difficult to achieve desired classification results when training samples are imbalanced. Sample imbalance is common in the source identification of mine water-inrush. Therefore, we propose a three-dimensional (3D) spatial resampling method based on rare water quality samples, which achieves the balance of water quality samples. Based on the virtual water sample points distributed by the 3D grid, the method uses the 3D Inverse Distance Weighting (IDW) method to interpolate the groundwater ion concentration of the virtual water samples to achieve oversampling of rare water samples. Case study in Gubei Coal Mine shows that the method improves overall discriminant accuracy of the Bayesian criterion model by 5.26%, from 85.26% to 90.69%. In particular, the discriminative precision of the rare class is improved from 0% to 83.33%, which indicates that the method can improve the discriminant accuracy of the rare class to large extent. In addition, this method increases the Kappa coefficient of the model by 19.92%, from 52.26% to 72.19%, increasing the degree of consistency from “general” to “significant”. Our research is of significance to enriching and improving the theory of prevention and treatment of mine water damage.

In an effort to achieve an optimal availability time of induction motors via fault probabilities reduction and improved prediction or diagnostic tools responsiveness, a conditional probabilistic approach was used. So, a Bayesian network (BN) has been developed in this paper. The objective will be to prioritize predictive and corrective maintenance actions based on the definition of the most probable fault elements and to see how they serve as a foundation for the decision framework. We have explored the causes of faults for an induction motor. The influence of different power ranges and the criticality of the electric induction motor are also discussed. With regard to the problem of induction motor faults monitoring and diagnostics, each technique developed in the literature concerns one or two faults. The model developed, through its unique structure, is valid for all faults and all situations. Application of the proposed approach to some machines shows promising results on the practical side. The model developed uses factual information (causes and effects) that is easy to identify, since it is best known to the operator. After that comes an investigation into the causal links and the definition of the a priori probabilities. The presented application of Bayesian networks is the first of its kind to predict faults of induction motors. Following the results of the inference obtained, prioritizations of the actions can be carried out.

Admixtures are commonly used nowadays in the mix composition of concrete. These additions affect concrete properties and performance especially creep deformations. This paper shows the effect of admixtures on creep of concrete. In fact, creep deformations have prejudicial consequences on concrete behaviour; an incorrect or inaccurate prediction leads to undesirable consequences in structures. Therefore, an accurate estimation of these deformations is mandatory. Moreover, design codes do not consider admixtures’ effect while predicting creep deformations, thus it is necessary to develop models that predict accurately creep deformations and consider the effect of admixtures. Using a large experimental database coming from international laboratories and research centres, this study aims to update the Eurocode 2 creep model by considering the type and percentage of admixtures using Bayesian Linear Regression method. The effect of two types of admixtures is presented in this paper; the water reducer and silica fume.

Human brain is “the perfect guessing machine” (James V. Stone (2012) Vision and Brain, Cambridge, Mass: The MIT Press, p. 155), trying to interpret sensory data in the light of previous biases or beliefs. Bayesian inference is carried out by three complex networks of the human brain: salience network, central executive network, and default mode network. Their function is analysed both in neurotypical person and Attention Deficit Disorder. Modern human being having predictive brain and overloaded mind must develop social identity, whose evolution went probably through three stages: social selection based on punishment, sexual selection based on reputation, and group selection based on identity.