Bayesian Statistical Theory Research Articles

The audience effect and the role of deception in the expression of male mating preferences

Males of several species have been shown to alter their mate preference in the presence of an eavesdropping rival. This evasive tactic has been interpreted as an attempt by the courting male to drive the attention of the rival away from the preferred female. The fitness return of this deceptive strategy will depend on the costs of cheating for the actor (the displayer) and the benefits for the rival (the bystander) of copying the choice of the displayer. We developed a two-person nonzero sum game between two males that compete for mating with one of two receptive females. Males could assess female quality with a varying level of uncertainty, which was modelled using a Bayesian statistical decision theory approach. We explored the actor and bystander payoffs under different levels of uncertainty in mate assessment and difference in quality between females. We found that when being eavesdropped on is costly (i.e. when females differ largely in quality), males are expected to cheat to reduce the amount of public information that is available to the unintended audience. However, under these circumstances, the value of the public information is low and the bystander is not expected to copy the choice of the actor. Our model suggests that deceptive male choice may evolve only under relatively restricted conditions and suggest that other explanations, such as, for example, a reduction in the risk of precopulatory male–male competition may be more likely. Future theoretical and empirical work will be necessary to test alternative interpretations of the audience effects in male mate choice.

Modeling and Calculation of Dent Based on Pipeline Bending Strain

The bending strain of long-distance oil and gas pipelines can be calculated by the in-line inspection tool which used inertial measurement unit (IMU). The bending strain is used to evaluate the strain and displacement of the pipeline. During the bending strain inspection, the dent existing in the pipeline can affect the bending strain data as well. This paper presents a novel method to model and calculate the pipeline dent based on the bending strain. The technique takes inertial mapping data from in-line inspection and calculates depth of dent in the pipeline using Bayesian statistical theory and neural network. To verify accuracy of the proposed method, an in-line inspection tool is used to inspect pipeline to gather data. The calculation of dent shows the method is accurate for the dent, and the mean relative error is 2.44%. The new method provides not only strain of the pipeline dent but also the depth of dent. It is more benefit for integrity management of pipeline for the safety of the pipeline.

Uncertainty assessment in building energy performance with a simplified model

To assess a building energy performance, the consumption being predicted or estimated during the design stage is compared to the measured consumption when the building is operational. When valuing this performance, many buildings show significant differences between the calculated and measured consumption. In order to assess the performance accurately and ensure the thermal efficiency of the building, it is necessary to evaluate the uncertainties involved not only in measurement but also those induced by the propagation of the dynamic and the static input data in the model being used. The evaluation of measurement uncertainty is based on both the knowledge about the measurement process and the input quantities which influence the result of measurement. Measurement uncertainty can be evaluated within the framework of conventional statistics presented in the Guide to the Expression of Measurement Uncertainty (GUM) as well as by Bayesian Statistical Theory (BST). Another choice is the use of numerical methods like Monte Carlo Simulation (MCS). In this paper, we proposed to evaluate the uncertainty associated to the use of a simplified model for the estimation of the energy consumption of a given building. A detailed review and discussion of these three approaches (GUM, MCS and BST) is given. Therefore, an office building has been monitored and multiple temperature sensors have been mounted on candidate locations to get required data. The monitored zone is composed of six offices and has an overall surface of 102 m2 .

Observation Value Analysis – Integral Part of Bayesian Diagnostics

The decision making process, in general, is understood as a process of selecting one of the available solutions to the problem. One of possible approaches supporting the process is Bayesian statistical decision theory providing a mathematical model to make decisions of a technical nature in conditions of uncertainty. Regarding above, a detailed subject of the research is to analyze the value of the observation, which is a part of preposterior analysis. For the presented network, the main objective was to determine, conducting of which of three tests is the most valuable from the perspective of determining possible need or possibility to omission expensive technical expertise. The basis of verification, which test is the most valuable, is therefore the comparison of expected value of sample information (EVSI) for each of the three tests.The main advantage of preposterior analysis is answering the question which of the considered experiments is potentially the best source of information and what cost needs to be incurred on its execution (price of new information). If the cost of such an examination is relatively small compared to the value of information on the state of nature, this implies a direct recommendation to conduct the experiment.In conclusion, the construction of the decision-making model being a reflection of the state of nature allows to determine the ranking of decisions, including those regarding selection of the optimal test. It is noteworthy, that test result itself can contribute to an increase in the expected value of the decision involved, but on the other hand, the reduction of uncertainty may be considerably outweighed to the necessity to incur expenses for this examination.

Model Optimization and Testing on Real-Time Video Moving Target Detection

Zhang presented a statistical model of real-time video moving target detection based on Bayesian statistical theory. This article discusses the algorithm parameter selection and detection efficiency of the model by using the experimental simulation method. This article generates a reference background based on unsupervised learning methods, and uses a color space that has a better environmental adaptability to represent the background, and uses dynamic threshold method to classify the results of background subtraction and frame difference. By comparing experimental of different methods, it shows that this algorithm has a greater advantage in terms of accuracy and timeliness.

Study on Probabilistic Identification Method of Structural Physical Parameter Based on Bayesian Estimation

Base on the structural modal parameters, the linear regression models of physical parameters are derived from the dynamic equations. The posterior joint Probability Density Function (PDF) of physical parameters is obtained by using Bayesian statistics theory. Then the Markov Chain Monte Carlo (MCMC) sample method is adopted to get the marginal PDF and optimal estimation of structural physical parameters. A numerical simulation of a 5-stroy structure under the excitation of white noise with different noise level is used to identify the physical parameters by the presented method. It is shown that the method can not only determine the optimal estimate but also get the probability distribution of the structural physical parameters from the known modal parameters of the primary modes. The method is with higher validity, robustness and efficiency, and can be applied to the structural health monitoring, damage evaluation, and so on.

A Statistical Model for Real-Time Video Moving Target Detection Based on Bayesian Statistics

Video moving target detection is an important foundation issues in computer vision, based on the analysis of the advantages and disadvantages of each existing moving target detection model, using Bayesian statistical theory as a framework, proposes a statistical model that can detect moving objects in video in real-time. The model combines time, space and color and other relevant information of pixel, divides and extracts Video segmentation’s foreground. By selecting the appropriate reference background can improve the precision and accuracy of the detection.

Optimism as a prior belief about the probability of future reward.

Optimists hold positive a priori beliefs about the future. In Bayesian statistical theory, a priori beliefs can be overcome by experience. However, optimistic beliefs can at times appear surprisingly resistant to evidence, suggesting that optimism might also influence how new information is selected and learned. Here, we use a novel Pavlovian conditioning task, embedded in a normative framework, to directly assess how trait optimism, as classically measured using self-report questionnaires, influences choices between visual targets, by learning about their association with reward progresses. We find that trait optimism relates to an a priori belief about the likelihood of rewards, but not losses, in our task. Critically, this positive belief behaves like a probabilistic prior, i.e. its influence reduces with increasing experience. Contrary to findings in the literature related to unrealistic optimism and self-beliefs, it does not appear to influence the iterative learning process directly.

Positioning Algorithm of Underground Personnel Based on ZigBee Technology

In recent years, people pay more and more attention to coal production safety. In order to enhance the security of underground work, optimize the coal mine management, and improve the personnel location accuracy, a newly arisen technique based on ZigBee with the low power, low cost and low complexity, which is suitable for being used to implement personnel position in underground coal mine, is built after comparing with several kinds of major wireless technology. In this paper, we present a new algorithm of underground personnel position based on this new technology. The algorithm uses Gaussian filtering algorithm and Bayesian statistics theory. The algorithm has been tested and the result has been analyzed. The experimental results show that the proposed algorithm provides more accurate position than positioning algorithm using RSSI.

Robust Reliability Design of Banjo Flange Based on Bayesian Theory

The perturbation method, bayesian statistics theory, the reliability optimization design, the reliability sensitivity technique and robust design are employed to present a practical and effective approach for the robust reliability design of the Banjo flange under incomplete information. The theoretical formulae of robust reliability design for the Banjo flange under incomplete information are obtained. The respective program can be used to obtain the robust reliability design parameters of the Banjo flange under incomplete information accurately and quickly.

Bayesian methods for outliers detection in GNSS time series

This article is concerned with the problem of detecting outliers in GNSS time series based on Bayesian statistical theory. Firstly, a new model is proposed to simultaneously detect different types of outliers based on the conception of introducing different types of classification variables corresponding to the different types of outliers; the problem of outlier detection is converted into the computation of the corresponding posterior probabilities, and the algorithm for computing the posterior probabilities based on standard Gibbs sampler is designed. Secondly, we analyze the reasons of masking and swamping about detecting patches of additive outliers intensively; an unmasking Bayesian method for detecting additive outlier patches is proposed based on an adaptive Gibbs sampler. Thirdly, the correctness of the theories and methods proposed above is illustrated by simulated data and then by analyzing real GNSS observations, such as cycle slips detection in carrier phase data. Examples illustrate that the Bayesian methods for outliers detection in GNSS time series proposed by this paper are not only capable of detecting isolated outliers but also capable of detecting additive outlier patches. Furthermore, it can be successfully used to process cycle slips in phase data, which solves the problem of small cycle slips.

An evaluation of tsunami hazard using Bayesian approach in the Indian Ocean

The present study deals with the estimation of tsunami hazard parameters (maximum regional tsunami intensity Imax, slope β-value (where, β=b/log10e) of tsunami intensity–frequency relationship and tsunami activity rate λ) in the whole Indian Ocean as well as Andaman–Sumatra–Java (ASJ) region using Bayesian statistics technique. For this purpose a reliable, homogeneous and complete tsunami catalog during the period 1797 to 2006 with tsunami intensities (Soloviev–Imamura intensity scale) I≥2.0, having average wave heights H≥2.83m, is utilized. The slope (b-value) of linear tsunami intensity–frequency relationship (Log10N=a−b∗I) of G–R type for observed tsunami data with intensities I≥2.0 is calculated as 0.41. The applied method of Bayesian statistics follow three assumptions viz. Poissonian character of tsunami events, existence of the frequency–intensity relation of G–R type with a cut-off maximum value of tsunami intensities and catalog contains rather sizable events. In this study, the maximum regional tsunami intensity (Imax) has been estimated as 5.39±0.30 and 5.41±0.31 for the whole Indian Ocean and ASJ regions, respectively, which is reasonably comparable to the maximum observed tsunami intensity of 5.0 for August 27, 1883 Indonesia tsunami. The slope β-value of tsunami intensity–frequency relationship is calculated as 0.81 for both the Indian Ocean and ASJ regions by Bayesian statistics theory. We have also estimated quantiles of true and apparent tsunami intensities for future time intervals of 20, 30, 40 and 50years with confidence limits for probability levels of 50%, 60%, 70%, 80%, 90% and 95% in the Indian Ocean along with ASJ region. It is observed that the differences between true and apparent quantiles of tsunami intensities are negligible revealing that a good quality of tsunami data is used in the study. The results estimated in the present study have potential useful implications in the probabilistic tsunami hazard assessment in the Indian Ocean region.

Bayesian Reliability-Based Optimization Design of Torsion Bar

Based on bayesian statistics theory and reliability-based optimization design, the research presents a new reliability-based optimization design approach that solves the form of finite test samples. In the article, the bayesian reliability-based optimization mathematical model is established and the bayesian reliability-based optimization approach of torsion bar is proposed. The method adopts a bayesian inference technique to estimate reliability, gives definition of bayesian reliability. The results illustrates the method presented is an efficient and practical reliability-based optimization approach of torsion bar.

Dynamic Bayesian identification of mechanical parameters of multi-cell curve box girder based on conjugate gradient theory

For multi-cell curve box girder, the finite strip governing equation was derived on the basis of Novozhilov theory and orthogonal property of harmonious function series. Dynamic Bayesian error function of mechanical parameters of multi-cell curve box girder was achieved with Bayesian statistical theory. The corresponding formulas of dynamic Bayesian expectation and variance were obtained. After the one-dimensional optimization search method for the automatic determination of step length of the mechanical parameter was put forward, the optimization identification calculative formulas were also obtained by adopting conjugate gradient method. Then the steps of dynamic Bayesian identification of mechanical parameters of multi-cell curve box girder were stated in detail. Through analysis of a classic example, the dynamic Bayesian identification processes of mechanical parameters are steadily convergent to the true values, which proves that dynamic Bayesian theory and conjugate gradient theory are suitable for the identification calculation and the compiled procedure is correct. It is of significance that the foreknown information of mechanical parameters should be set with reliable practical engineering experiences instead of arbitrary selection.

Probabilistic sensitivity analysis in health economics

Health economic evaluations have recently become an important part of the clinical and medical research process and have built upon more advanced statistical decision-theoretic foundations. In some contexts, it is officially required that uncertainty about both parameters and observable variables be properly taken into account, increasingly often by means of Bayesian methods. Among these, probabilistic sensitivity analysis has assumed a predominant role. The objective of this article is to review the problem of health economic assessment from the standpoint of Bayesian statistical decision theory with particular attention to the philosophy underlying the procedures for sensitivity analysis.

Powell dynamic identification of displacement parameters of indeterminate thin-walled curve box based on FCSE theory

The FCSE controlling equation of pinned thin-walled curve box was derived and the indeterminate problem of continuous thin-walled curve box with diaphragm was solved based on flexibility theory. With Bayesian statistical theory, dynamic Bayesian error function of displacement parameters of indeterminate curve box was founded. The corresponding formulas of dynamic Bayesian expectation and variance were deduced. Combined with one-dimensional Fibonacci automatic search scheme of optimal step size, the Powell optimization theory was utilized to research the stochastic identification of displacement parameters of indeterminate thin-walled curve box. Then the identification steps were presented in detail and the corresponding calculation procedure was compiled. Through some classic examples, it is obtained that stochastic performances of systematic parameters and systematic responses are simultaneously deliberated in dynamic Bayesian error function. The one-dimensional optimization problem of the optimal step size is solved by adopting Fibonacci search method. And the Powell identification of displacement parameters of indeterminate thin-walled curve box has satisfied numerical stability and convergence, which demonstrates that the presented method and the compiled procedure are correct and reliable. During parameters’ iterative processes, the Powell theory is irrelevant with the calculation of finite curve strip element (FCSE) partial differentiation, which proves high computation efficiency of the studied method.

BAYESIAN IMAGE SEGMENTATION THROUGH LEVEL LINES SELECTION

Bayesian statistical theory is a convenient way of taking a priori information into consideration when inference is made from images. In Bayesian image segmentation, the a priori distribution should capture the knowledge about objects. Taking inspiration from (Alvarez et al., 1999), we design a prior density that penalizes the area of homogeneous parts in images. The segmentation problem is further formulated as the estimation of the set of curves that maximizes the posterior distribution. In this paper, we explore a posterior distribution model for which its maximal mode is given by a subset of level curves, that is the boundaries of image level sets. For the completeness of the paper, we present a stepwise greedy algorithm for computing partitions with connected components.

The 3-D tectonic stress fields in and around Japan inverted from centroid moment tensor data of seismic events

[1] We can regard the occurrence of earthquakes as the partial release of tectonic stress by sudden brittle rupture. In the framework of linear elasticity, any indigenous source including earthquake rupture is represented by a moment tensor. The moment tensor is mathematically equivalent to the volume integral of stress release over the whole elastic region surrounding the source, and so we can quantitatively relate the centroid moment tensor (CMT) of seismic events with an unknown tectonic stress field. On the basis of such an idea and Bayesian statistical inference theory, we developed an inversion method to estimate the 3-D pattern of tectonic stress from CMT data. Applying the CMT data inversion method to 12,500 seismic events in and around Japan, we obtained precise 3-D tectonic stress patterns that illuminate the present-day (Quaternary) complex tectonic motion of Japanese islands. The stress pattern of the Kuril-Japan-Nankai arc is basically E–W compression, but the direction of intermediate principal stress changes from N–S (reverse faulting type) in northeast Japan to vertical (strike-slip faulting type) in southwest Japan. On the other hand, the stress pattern of the Ryukyu and Izu-Bonin back-arc regions is basically trench perpendicular tension (normal faulting type). In addition to these basic stress patterns governed by mechanical interaction between the Eurasian, North American, Pacific, and Philippine Sea plates, we can recognize several characteristic local stress patterns corresponding to the horizontal motion of the Kuril fore-arc sliver, the collision of the Izu Peninsula with the mainland of Japan, and the opening of the Beppu-Shimabara rift zone.

Predictions of visual performance from the statistical properties of natural scenes

Event Abstract Back to Event Predictions of visual performance from the statistical properties of natural scenes Wilson Geisler1* 1 University of Texas, United States Five decades ago Horace Barlow argued that sensory scientists should explore the relationship between the design of an organism’s sensory circuits, the organism’s natural tasks, and the stimulus properties relevant to those tasks. In the past two decades advances in physical measurement technology, computational power and statistical modeling have made it possible to begin exploring this relationship in detail. In this talk I will briefly summarize our recent efforts to determine what stimulus features are optimal for performance in specific visual tasks, how those features should be combined to optimally perform those tasks, and how human performance compares with optimal performance. Our methods for determining optimal stimulus features and optimal performance are both based on the concepts of Bayesian statistical decision theory. Our results suggest that a quantitative analysis of the natural scene statistics that support natural tasks can often provide novel quantitative predictions of visual performance and deep insight into the design of the visual system. Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010. Presentation Type: Oral Presentation Topic: Oral presentations Citation: Geisler W (2010). Predictions of visual performance from the statistical properties of natural scenes. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00025 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Feb 2010; Published Online: 17 Feb 2010. * Correspondence: Wilson Geisler, University of Texas, Austin, United States, geisler@psy.utexas.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Wilson Geisler Google Wilson Geisler Google Scholar Wilson Geisler PubMed Wilson Geisler Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

A few prospective ideas on climate reconstruction: from a statistical single proxy approach towards a multi-proxy and dynamical approach

Abstract. Important progresses have been made in palaeoclimatological studies by using statistical methods. But they are in somewhere limited as they take the present as an absolute reference. This is particularly true for the modern analogue technique. The availability of mechanistic models to simulate the proxies measured in the sediment cores gives now the possibility to relax this constraint. In particular, vegetation models provide outputs comparable to pollen data (assuming that there is a relationship between plant productivity and pollen counts). The input of such models is, among others, climate. The idea behind paleoclimatological reconstructions is then to obtain inputs, given outputs. This procedure, called model inversion, can be achieved with appropriate algorithms in the frame of the Bayesian statistical theory. But we have chosen to present it in an intuitive way, avoiding the mathematics behind it. Starting from a relative simple application, based on an equilibrium BIOME3 model with a single proxy (pollen), the approach has evolved into two directions: (1) by using several proxies measured on the same core (e.g. lake-level status and δ13C) when they are related to a component of the vegetation, and (2) by using a more complex vegetation model, the dynamic vegetation model LPJ-GUESS. Examples presented (most of them being already published) concern Last Glacial Maximum in Europe and Africa, Holocene in a site of the Swiss Jura, an Eemian site in France. The main results are that: (1) pollen alone is not able to provide exhaustive information on precipitation, (2) assuming past CO2 equivalent to modern one may induce biases in climate reconstruction, (3) vegetation models seem to be too much constrained by temperature relative to precipitation in temperate regions. This paper attempts to organise some recent ideas in the palaeoclimatological reconstruction domain and to propose prospectives in that effervescent domain.