Bayesian methods Research Papers - Academia.edu (original) (raw)

Statistical hypotheses testing is one of the basic direction of mathematical statistics the methods of which are widely used in theoretical research and practical applications. These methods are widely used in medical researches too.... more

Statistical hypotheses testing is one of the basic direction of mathematical statistics the methods of which are widely used in theoretical research and practical applications. These methods are widely used in medical researches too. Scientists of different fields, among them of medical too, that are not experts in statistics, are often faced with the dilemma of which method to use for solving the problem they are interested. The article is devoted to helping the specialists in solving this problem and in finding the optimal resolution. For this purpose, here are very simple and clearly explained the essences of the existed approaches and are shown their positive and negative sides and are given the recommendations about their use depending on existed information and the aim that must be reached as a result of an investigation.

In this paper, we investigate the effectiveness of a financial time-series forecasting strategy which exploits the mul- tiresolution property of the wavelet transform. A financial series is decomposed into an over complete, shift... more

In this paper, we investigate the effectiveness of a financial time-series forecasting strategy which exploits the mul- tiresolution property of the wavelet transform. A financial series is decomposed into an over complete, shift invariant scale-related representation. In transform space, each individual wavelet series is modeled by a separate multilayer perceptron (MLP). To better utilize the detailed information in the lower scales of wavelet coef- ficients (high frequencies) and general (trend) information in the higher scales of wavelet coefficients (low frequencies), we applied the Bayesian method of automatic relevance determination (ARD) to choose short past windows (short-term history) for the inputs to the MLPs at lower scales and long past windows (long-term history) at higher scales. To form the overall forecast, the indi- vidual forecasts are then recombined by the linear reconstruction property of the inverse transform with the chosen autocorrelation shell representatio...

Knowledge about current patterns of genetic structure of populations together with the evolutionary history of a species helps to understand and predict the adaptation of populations to future climate change. We assayed variation at... more

Knowledge about current patterns of genetic structure of populations together with the evolutionary history of a species helps to understand and predict the adaptation of populations to future climate change. We assayed variation at nuclear microsatellite markers among peripheral vs. continuous populations of the temperate South American species Austrocedrus chilensis, to investigate the role of historical vs. demographical forces in shaping population genetic structure. This species occurs in continuous populations in the west and central distribution range, but becomes highly fragmented at the eastern limit, which comprised ice-free areas during Quaternary glaciations and has extreme climatic conditions at present times. Bayesian analysis methods identified two contrasting patterns of genetic structure; (I) populations from humid, mesic and peri-glacial regions formed a single deme with relatively low genetic differentiation and high admixture levels whereas (II) a highly heterogeneous genetic structure with low level of admixture was found in the steppe, towards the east and northeast limit of the distribution range. In the steppe, population fragmentation, restricted gene flow and isolation-by-distance were also inferred. In addition, several small steppe populations showed high genetic diversity and divergent gene pools, suggesting that they constitute ancient refuges from pre-Holocene glaciations with just a subgroup of them contributing significantly to post-glacial spread. These results are discussed in relation to patterns of genetic variation found for other temperate species and the contribution of the particular southern Andes topography and climate to post-glacial spread.

The performance of target identification can be improved by fusing the data from multiple sensors. Even though distributed fusion has advantages of lower communication bandwidth, less processing at a central location, and increased... more

The performance of target identification can be improved by fusing the data from multiple sensors. Even though distributed fusion has advantages of lower communication bandwidth, less processing at a central location, and increased robustness over centralized fusion, it has to address technical issues such as the conditional dependence of information to be fused by a fusion agent. This paper presents distributed fusion and communication management algorithms for target identification. Information graphs are used to select fusion architectures that minimize the effect of information double counting due to communication. Bayesian networks are used to model the target identification problem and identify the sufficient information that needs to be communicated between processing agents for optimal fusion. Communication strategies are developed to determine when a fusion agent should communicate with another fusion agent. Simulation examples demonstrate the performance of distributed fus...

Developing hardware, algorithms and protocols, as well as collecting data in sensor networks are all important challenges in building good systems. We describe a vertical system integration of a sensor node and a toolkit of machine... more

Developing hardware, algorithms and protocols, as well as collecting data in sensor networks are all important challenges in building good systems. We describe a vertical system integration of a sensor node and a toolkit of machine learning algorithms. Based on a dataset that combines sensor data with additional introduced data we predict the number of persons in a closed space.

Remote Sensing images exhibit an enormous amount of information. In order to extract this information in a robust way and to make it available as efficient indices for query by image content, we present a scheme of hierarchical stochastic... more

Remote Sensing images exhibit an enormous amount of information. In order to extract this information in a robust way and to make it available as efficient indices for query by image content, we present a scheme of hierarchical stochastic description. The different levels in this hierarchy are derived from the different levels of abstraction: image data (0), image features (1), meta features (2), image classification (3), geometric features (4), and user-specific semantics (5). We describe this hierarchical scheme and the processes of Bayesian inference between these levels and present a case study using synthetic aperture radar (SAR) data.

This paper presents an application of Bayesian networks (BN) to the problem of reliability assessment of power systems. Bayesian networks provide a flexible means of representing and reasoning with probabilistic information. Uncertainty... more

This paper presents an application of Bayesian networks (BN) to the problem of reliability assessment of power systems. Bayesian networks provide a flexible means of representing and reasoning with probabilistic information. Uncertainty and dependencies are easily incorporated in the analysis. Efficient probabilistic inference algorithms in Bayesian networks permit not only computation of the loss of load probability but also answering various probabilistic queries about the system. The advantages of BN models ...

Credibility - the believability of new findings in the light of current knowledge - is a key issue in the assessment of clinical trial outcomes. Yet despite the growth of evidence-based medicine, credibility is usually dealt with in a... more

Credibility - the believability of new findings in the light of current knowledge - is a key issue in the assessment of clinical trial outcomes. Yet despite the growth of evidence-based medicine, credibility is usually dealt with in a broad-brush and qualitative fashion. This paper describes how Bayesian methods lead to quantitative credibility assessments that take explicit account of prior insights and experience. A simple technique based on the concept of the Critical Prior Interval (CPI) is presented which allows rapid credibility assessment of trial outcomes reported in the standard format of Odds Ratios and 95% confidence intervals. The CPI is easily determined via a graph, and provides clinicians with an explicit and objective base-line on which to base their assessment of credibility. The use of the CPI is demonstrated through several worked examples.