Search results for "Bayesian [statistical analysis]"
showing 10 items of 299 documents
A probabilistic expert system for predicting the risk of Legionella in evaporative installations
2011
Research highlights? The bacterium Legionella usually lives in water sources such as cooling towers. ? We discuss a probabilistic expert system for predicting the risk of Legionella. ? The expert system has a master-slave architecture. ? The inference engine is implemented through Bayesian reasoning. ? Bayesian networks model and connect relationships for chemical and physical variables. Early detection in water evaporative installations is one of the keys to fighting against the bacterium Legionella, the main cause of Legionnaire's disease. This paper discusses the general structure, elements and operation of a probabilistic expert system capable of predicting the risk of Legionella in rea…
Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia
2020
Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is crucial to take advantage of this potential. The field of machine learning offers algorithms for both parameter selection and combination. In this study, several established machine learning approaches including a method for the selection of suitable signal parameters and classification algorithms are a…
Assessment of the impacts of an oil spill on the populations of common guillemot (Uria aalge) and long-tailed duck (Clangula hyemalis) - an expert kn…
2012
The amount of operated oil transports continues to increase in the Gulf of Finland and in the case of an accident hazardous amounts of oil may be spilled into the sea. The oil accident may be harmful for the common guillemot and long-tailed duck populations. In this study expert knowledge regarding the behaviour and population dynamics of common guillemot and long-tailed duck in the Gulf of Finland was used to build a model to assess the impacts of an oil spill on the mortality and population size of these species. The Bayesian networks were used in the modelling. Based on the results the breeding colony of guillemots in Aspskär may survive in the consequence of recolonization. In conclusio…
A model of adaptive decision-making from representation of information environment by quantum fields
2017
We present the mathematical model of decision making (DM) of agents acting in a complex and uncertain environment (combining huge variety of economical, financial, behavioral, and geo-political factors). To describe interaction of agents with it, we apply the formalism of quantum field theory (QTF). Quantum fields are of the purely informational nature. The QFT-model can be treated as a far relative of the expected utility theory, where the role of utility is played by adaptivity to an environment (bath). However, this sort of utility-adaptivity cannot be represented simply as a numerical function. The operator representation in Hilbert space is used and adaptivity is described as in quantu…
Optimizing channel selection for cognitive radio networks using a distributed Bayesian learning automata-based approach
2015
Consider a multi-channel Cognitive Radio Network (CRN) with multiple Primary Users (PUs), and multiple Secondary Users (SUs) competing for access to the channels. In this scenario, it is essential for SUs to avoid collision among one another while maintaining efficient usage of the available transmission opportunities. We investigate two channel access schemes. In the first model, an SU selects a channel and sends a packet directly without Carrier Sensing (CS) whenever the PU is absent on this channel. In the second model, an SU invokes CS in order to avoid collision among co-channel SUs. For each model, we analyze the channel selection problem and prove that it is a so-called "Exact Potent…
Flexible Bayesian survival models with application in biometric studies
2018
El análisis de supervivencia es una metodología estadística diseñada para analizar datos procedentes de estudios científicos relativos a tiempos de ocurrencia de uno o varios eventos de interés. La duración de estos tiempos suele conocerse como tiempos de supervivencia debido a los particulares orígenes de esta metodología en contextos exclusivamente médicos y demográficos. Durante las últimas décadas, la literatura científica en este campo ha sido muy prolífica y su aplicación se ha extendido a múltiples áreas de conocimiento. Los procedimientos estadísticos propios de esta metodología empezaron a abordarse desde el marco inferencial frecuentista. Sin embargo, en los últimos años la utiliz…
Consistency of Probability Decision Rules and Its Inference in Probability Decision Table
2012
In most synthesis evaluation systems and decision-making systems, data are represented by objects and attributes of objects with a degree of belief. Formally, these data can be abstracted by the form (objects; attributes; P), wherePrepresents a kind degree of belief between objects and attributes, such that,Pis a basic probability assignment. In the paper, we provide a kind of probability information system to describe these data and then employ rough sets theory to extract probability decision rules. By extension of Dempster-Shafer evidence theory, we can get probabilities of antecedents and conclusion of probability decision rules. Furthermore, we analyze the consistency of probability de…
Accelerated Bayesian learning for decentralized two-armed bandit based decision making with applications to the Goore Game
2012
Published version of an article in the journal: Applied Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/s10489-012-0346-z The two-armed bandit problem is a classical optimization problem where a decision maker sequentially pulls one of two arms attached to a gambling machine, with each pull resulting in a random reward. The reward distributions are unknown, and thus, one must balance between exploiting existing knowledge about the arms, and obtaining new information. Bandit problems are particularly fascinating because a large class of real world problems, including routing, Quality of Service (QoS) control, game playing, and resource allocation, can be solved …
What drives German foreign direct investment? New evidence using Bayesian statistical techniques
2019
Abstract Despite the importance of Germany as an issuer of foreign direct investment (FDI), the studies analyzing its determinants are far from conclusive. This research contributes to filling this gap providing new evidence for the period 1996–2012. In order to reduce model uncertainty, we adopt a Bayesian model averaging (BMA) approach. We find that determinants associated with horizontal FDI appear to be dominant for explaining FDI in developed countries while for the group of developing countries covariates associated with vertical FDI motives play a larger role. Within Europe, while the majority of FDI is horizontally driven in “core” countries, in the “periphery” vertical motivations …
Ethics of Beliefs
2017
This paper deals with the concept of positive learning (PL). The main goal is to provide a working definition of PL on which further refinements and extensions can be based. First, I formulate a list of desiderata for a definition of PL: I argue that a working definition of PL should (i) make the involved epistemic norms explicit, (ii) be flexible, and (iii) be empirically tractable. After that, I argue that a working definition of PL should focus on three basic epistemic norms (which I call Evidentialism, Degrees of Plausibility, and Non-Arbitrary Updates). Drawing on work on the ethics of belief and Bayesian inference, I highlight theoretical and empirical challenges that already follow f…