Introduction Of Prior Information Research Articles

Dynamic data driven load-carrying capacity prediction method for composite laminates with delamination

The delamination of composites has invisibility and can reduce the load-carrying capacity (LCC) of structures. In this paper, a dynamic data driven LCC prediction method for composite laminates considering delamination is proposed. The method is divided into offline and online phases: In the offline phase, finite element modeling, prior information acquisition, and database construction are carried out. In the online phase, the Bayes factor is used to realize the rapid mapping between sensor data and the sample in the database to predict the LCC of the structure. In numerical cases with noise, the prediction errors of LCC are within 2.3% with sensor data, and the introduction of prior information can improve efficiency and accuracy of the prediction. The material parameters and delamination sizes of the identification results are the samples closest to the given solution in the database. In test cases, the information from strain arrays can effectively reflect the delamination growth of the laminate. After three times data assimilation, the prediction errors of LCC are within 10.5%.

Prior information guided auto-contouring of breast gland for deformable image registration in postoperative breast cancer radiotherapy.

Contouring of breast gland in planning CT is important to postoperative radiotherapy of patients after breast conserving surgery (BCS). However, the contouring task is difficult because of the poorer contrast of breast gland in planning CT. To improve its efficiency and accuracy, prior information was introduced in a 3D U-Net model to predict the contour of breast gland automatically. The preoperative CT was first aligned to the planning CT via affine registration. The resulting transform was then applied to the contour of breast gland in preoperative CT, and the corresponding contour in planning CT was obtained. This transformed contour was a preliminary estimation of breast gland in planning CT and was used as prior information in a 3D U-Net model to obtain a more accurate contour. For evaluation, the dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to assess the deep learning (DL) model's prediction accuracy. The average DSC and HD of the prediction model were 0.775±0.065 and 44.979±20.565 for breast gland without the input of prior information, while the average values were 0.830±0.038 and 17.896±5.737 with the input of prior information (0.775 vs. 0.830, P=0.0014<0.05; 44.979 vs. 17.896, P=0.002<0.05). The prediction accuracy was increased significantly with the introduction of prior information, which provided valuable geometrical distribution of target for model training. This method provides an effective way to identify low-contrast targets from surrounding tissues in CT and will be useful in other image modalities.

Mitigating the ill-posedness of first-arrival traveltime tomography using slopes: application to the eastern Nankai Trough (Japan) OBS data set

SUMMARYFirst-arrival traveltime tomography is one of the most used velocity model building techniques especially in sparse wide-angle acquisitions for deep crustal seismic imaging cases. Relying on the inversion of a picked attribute, the absolute traveltimes, the approach is ill-posed in terms of non-uniqueness of the solution. The latter is remedied by proper regularization or the introduction of prior information. Indeed, since traveltime kernels are vulnerable to the velocity–depth ambiguity, the inversion is stabilized by the introduction of complementary data like reflections and explicit reflectors in the velocity models. Here, we propose to supplement first-arrival traveltimes by their slopes, in other words the horizontal component of the slowness vectors at the sources and/or receivers. Slopes are a crucial attribute in state of the art scattering-based or reflection-based tomographic methods like slope tomography or wavefront tomography where the differential information is needed in order to locate the scattering events position or to parametrize the wavefront. The optional but valuable injection of slopes as an objective measure in first-arrival traveltime tomography stabilizes the problem by constraining the emergence angle or in turn implicitly the turning point depth of the rays. We explain why slopes have a tremendous added value in such a tomographic problem and highlight its remedial effect in cases where the medium is unevenly illuminated. We also show that the contribution of slopes become even more significant when the acquisition is sparse as it is generally the case with ocean-bottom seismometer surveys. The inferred models from such an extended time-attributes tomography will be used as initial guesses in a full-waveform inversion workflow context. The proposed strategy is benchmarked in 2-D media against a dip section of the SEG/EAGE overthrust model and then followed by a revisit of ocean bottom seismometers data from the eastern-Nankai subduction margin as a real deep crustal case study.

Direct updating of rock-physics properties using elastic full-waveform inversion

Quantitative estimation of rock-physics properties is an important part of reservoir characterization. Most current seismic workflows in this field are based on amplitude variation with offset. Building on recent work on high-resolution multiparameter inversion for reservoir characterization, we have constructed a rock-physics parameterized elastic full-waveform inversion (EFWI) scheme. Within a suitably formed multiparameter EFWI, in this case a 2D frequency-domain isotropic-elastic full-waveform inversion with a truncated Gauss-Newton optimization, any rock-physics model with a well-defined mapping between its parameters and seismic velocity/density can be examined. We select a three-parameter porosity, clay content, and water saturation (PCS) parameterization, and we link them to elastic properties using three representative rock-physics models: the Han empirical model, the Voigt-Reuss-Hill boundary model, and the Kuster and Toksöz inclusion model. Numerical examples suggest that conditioning issues, which make a sequential inversion (in which velocities and density are first determined through EFWI, followed by PCS parameters) unstable, are avoided in this direct approach. Significant variability in inversion fidelity is visible from one rock-physics model to another. However, the response of the inversion to the range of possible numerical optimization and frequency selections, as well as acquisition geometries, varies widely. Water saturation tends to be the most difficult property to recover in all situations examined. This can be explained with radiation pattern analysis, in which very low relative scattering amplitudes from saturation perturbations are observed. An investigation performed with a Bayesian approach illustrates that the introduction of prior information may increase the inversion sensitivity to water saturation.

Feature importance of the aortic anatomy on endovascular aneurysm repair (EVAR) using Boruta and Bayesian MCMC

A retrospective study of the abdominal aortic aneurysm (AAA) with EVAR treated patients. The third-party collected the data from twelve vascular centres in Indonesia during 2012-2017. Patient demographics and computed tomography data were evaluated with Osirix MD Software. During five years, we had 148 EVAR cases done using Endurant stent graft (Medtronic). In this paper, we perform Bayesian modelling and selection of feature selection by Boruta. Before performing the models, we will determine the selection of dependent variables start with the Age, Class, and Sex. It will get what is important to be dependent and independent. The difference between Bayesian and the classical method is the introduction of prior information in the form of probability distributions. In addition, to determine the parameters using the Bayesian method obtained from the probability statement. Parameter estimation in Bayesian is no longer a point estimate but, on the contrary, is a statistical distribution. In other words, Bayesian states that a parameter is a variable that has a distribution. Bayesian has become a popular method in modern statistical analysis. Bayesian is applied to a broad spectrum in the scientific and research fields. Bayesian data analysis involves learning from data that uses probability models for many observations and some information to be studied. In other words, analysing statistical models are by combining prior knowledge about the model or parameters of the model. In a nutshell, the simulation results obtained modelling with Bayesian-ZIP-MCMC R2 87.52 and Bayesian-Boruta R2 88.28%.

Fraction-Order Total Variation Image Blind Restoration Based on Self-Similarity Features

To improve the artifacts of the restoration results restored by existing blind restoration method, an effective image blind restoration method using self-similarity as prior information is proposed for restoring the blurry images. Firstly, the fraction-order model is achieved by extending integer-order total variation, which is prone to reduce artifacts. Motivated by the fact that the introduction of prior information is beneficial to improve the restoration results, we found that natural images usually exhibit some texture features. Self-similarity is a popular texture features and well-defined in the statistics. Therefore, this texture feature is introduced as prior information for the restoration model and further improving the restoration results. Finally, the cost function is generated and solved by semi-quadratic regularization. Experiments on various natural images showed that the proposed method can improve the performance relative to other image blind restoration algorithms in terms of both subjective vision and objective evaluation. The subjective analysis revealed that the proposed algorithm resulted in improved translation and improved artifact appearance. The objective evaluation showed that the proposed algorithm showed the best evaluation values, including Structural Similarity and Peak Signal-to-noise ratio. The restoration results of various images reveal that the proposed method is practical and effective in image restoration.

Application of an Adaptive Multi-Population Parallel Genetic Algorithm with Constraints in Electromagnetic Tomography with Incomplete Projections

Electromagnetic tomography technology (EMT) is widely used in underground energy exploration. Limited by objective conditions, the collected projection data of electromagnetic waves are sparse and incomplete. Therefore, a study of the tomographic inversion algorithm of EMT based on incomplete projection data has an important guiding significance for the exploitation of underground energy. As a global optimization probability search algorithm, the simple genetic algorithm (SGA) has been widely used in the process of tomographic inversion. However, SGA evolves through a single population, and the values of crossover and mutation probability are always fixed, so there are risks of premature convergence and poor local search ability. To improve the performance of the SGA, a new approach of adaptive multi-population parallel genetic algorithm (AMPGA) with constraints is proposed in this paper. First, the AMPGA makes full use of multi-group adaptive co-evolution to improve the local and global search ability of SGA and restrain the risk of premature convergence. Then, the introduction of prior information as a constraint makes the results clearer and more accurate. The proposed algorithm has been verified in a numerical experiment and field tests, and the results show that the proposed algorithm can well balance global and local search capabilities, which offers a more realistic and stable tomographic result.

Bayesian normal modes identification and estimation of elastic coefficients in resonant ultrasound spectroscopy

Resonant ultrasound spectroscopy is an experimental technique for measuring the stiffness of anisotropic solid materials. The free vibration resonant frequencies of a specimen are measured and the stiffness coefficients of the material adjusted to minimize the difference between experimental and predicted frequencies. An issue of this inverse approach is that the measured frequencies are not easily paired with their predicted counterpart, leading to ambiguities in the definition of the objective function. In the past, this issue has been overcome through trial-and-error methods requiring the experimentalist to find the correct pairing, or through involved experimental methods measuring the shapes of the normal vibration modes in addition to their frequencies. The purpose of this work is to show, through a Bayesian formulation, that the inverse problem can be solved automatically and without requiring additions to the usual experimental setup. The pairing of measured and predicted frequencies is considered unknown, and the joint posterior probability distribution of pairing and stiffness is sampled using Markov chain Monte Carlo. The method is illustrated on two published data sets. The first set includes the exact pairing, allowing validation of the method. The second application deals with attenuative materials, for which many predicted modes cannot be observed, further complicating the inverse problem. In that case, introduction of prior information through Bayesian formulation reduces ambiguities.

Inversion of ERT data with a priori information using variable weighting factors

In this work we propose a new way of introducing prior information regarding known resistivity distribution within the inversion procedure. Here the prior information is introduced as an extra term into the objective function of the resistivity inverse problem which is minimized via the Lagrangian multiplier technique. The final inversion equation allows the introduction of prior information in a flexible way. The contribution of prior information to the final inversion result can be weighted depending on the reliability of prior information. Further, a variable weighting scheme is proposed in order to avoid erroneous inversion results due to inaccurate prior information. The effectiveness of the new algorithm is demonstrated via synthetic and real data examples.

Testability demonstration test planning based on sequential posterior odds test method

Testability is a new critical characteristic of equipment, and testability demonstration test plays an important role in assuring the designed testability levels. As a result of the complex structure and higher cost of the current equipment, a reasonable testability demonstration test plan can both guarantee the desired testability level and effectively decrease the test cost. This article considers the testability demonstration test planning with the binomial distribution. The sequential posterior odds test method that combines Bayesian method and sequential probability ratio test method is used to form a sequential decision process by calculating the Bayes factor and making judgment according to the test result sequentially. With a sequential decision criterion, the test result space is divided into three subspaces, based on which a Bayes factor threshold calculation method is presented according to the restriction parameters and prior probability density function of the testability index. Operation characteristic and average sampling number are compared among the classical method, sequential probability ratio test method and the sequential posterior odds test method, which shows that with nearly the same operation characteristic, sequential posterior odds test method has smaller average sampling number than sequential probability ratio test method and classical method. The impact of prior parameters on the sequential posterior odds test method is analyzed, and the results indicate that the sequential posterior odds test method can perform well with a range of prior parameters. Physical testability demonstration tests are carried out to verify the features of sequential posterior odds test and sequential probability ratio test methods. The result indicates that with the same restriction parameters and test condition, the sample size of sequential posterior odds test is smaller than the sample size of sequential probability ratio test as a result of the introduction of prior information.

Can Bayesian models play a role in dental caries epidemiology? Evidence from an application to the BELCAP data set.

The aim of this study was to show the potential of Bayesian analysis in statistical modelling of dental caries data. Because of the bounded nature of the dmft (DMFT) index, zero-inflated binomial (ZIB) and beta-binomial (ZIBB) models were considered. The effects of incorporating prior information available about the parameters of models were also shown. The data set used in this study was the Belo Horizonte Caries Prevention (BELCAP) study (Böhning et al. (1999)), consisting of five variables collected among 797 Brazilian school children designed to evaluate four programmes for reducing caries. Only the eight primary molar teeth were considered in the data set. A data augmentation algorithm was used for estimation. Firstly, noninformative priors were used to express our lack of knowledge about the regression parameters. Secondly, prior information about the probability of being a structural zero dmft and the probability of being caries affected in the subpopulation of susceptible children was incorporated. With noninformative priors, the best fitting model was the ZIBB. Education (OR = 0.76, 95% CrI: 0.59, 0.99), all interventions (OR = 0.46, 95% CrI: 0.35, 0.62), rinsing (OR = 0.61, 95% CrI: 0.47, 0.80) and hygiene (OR = 0.65, 95% CrI: 0.49, 0.86) were demonstrated to be factors protecting children from being caries affected. Being male increased the probability of being caries diseased (OR = 1.19, 95% CrI: 1.01, 1.42). However, after incorporating informative priors, ZIB models' estimates were not influenced, while ZIBB models reduced deviance and confirmed the association with all interventions and rinsing only. In our application, Bayesian estimates showed a similar accuracy and precision than likelihood-based estimates, although they offered many computational advantages and the possibility of expressing all forms of uncertainty in terms of probability. The overdispersion parameter could expound why the introduction of prior information had significant effects on the parameters of the ZIBB model, while ZIB estimates remained unchanged. Finally, the best performance of ZIBB compared to the ZIB model was shown to catch overdispersion in data.

The use of predicted values for item parameters in item response theory models: an application in intelligence tests

In testing, item response theory models are widely used in order to estimate item parameters and individual abilities. However, even unidimensional models require a considerable sample size so that all parameters can be estimated precisely. The introduction of empirical prior information about candidates and items might reduce the number of candidates needed for parameter estimation. Using data for IQ measurement, this work shows how empirical information about items can be used effectively for item calibration and in adaptive testing. First, we propose multivariate regression trees to predict the item parameters based on a set of covariates related to the item-solving process. Afterwards, we compare the item parameter estimation when tree-fitted values are included in the estimation or when they are ignored. Model estimation is fully Bayesian, and is conducted via Markov chain Monte Carlo methods. The results are two-fold: (a) in item calibration, it is shown that the introduction of prior information is effective with short test lengths and small sample sizes and (b) in adaptive testing, it is demonstrated that the use of the tree-fitted values instead of the estimated parameters leads to a moderate increase in the test length, but provides a considerable saving of resources.

Some considerations on significance analysis for deformation detection via frequentist and Bayesian tests

The Bayesian approach to significance testing, that makes use of prior information, has been studied in the last years, particularly to allow the detection of deformations which are small with respect to measurement errors from repeated surveys. Some investigations showed up to now that, under particular conditions concerning both the parameters of the prior distribution and the structure of the control network, Bayesian tests give stronger results than tests based on the frequentist approach, with regard to the detectability of deformations. In the present paper, after illustrating the essential developments and the most relevant results of previous works, new analytical developments and numerical simulations are carried out, to illustrate some critical aspects and advantages of the frequentist and Bayesian methods. It is shown, starting from simple examples involving only Gaussian and truncated Gaussian distributions and from a simplified formulation of the prior deformation model, that the testing procedures based on the frequentist approach, which do not introduce any a priori information, are in many cases of practical interest insensitive to internal consistencies of the displacements (e.g., to the fact that they have a common direction). It is also illustrated that the introduction of prior information, even in a non Bayesian context, more easily allows the detection of little displacements. Besides it is noted that in some cases Bayesian tests detect displacements in the presence of observed non-coherent movements, even if prior probability densities describing coherent movements are introduced.

Response to Comment on “Two statistics for evaluating parameter identifiability and error reduction”

We appreciate the interest in the identifiability and relative parameter error variance reduction statistics that we describe in Doherty and Hunt (2009). Dr. Hill’s comments, referred to here as Hill (2010), include concerns specific to our paper, but also examples and issues that extend beyond our paper to the work of others. As such, they extend to broader methodological issues which underpin our general approach to model calibration and uncertainty analysis. In this regard they are similar to those raised in a recent paper (Hill, 2008a) and in a number of scientific presentations (Hill, 2006, 2007, 2008b, 2009). We consider a discussion of these issues vital to achieving changes in approaches to parameter estimation and uncertainty analysis that we believe will substantially enhance the ability of the modeling community to address societally relevant problems. We hope that discussion of the topics raised in Hill (2010) will increase the understanding of the approach that we are trying to advance, and are therefore pleased to engage in such a discussion. The material covered in Hill (2010), and therefore our response, encompasses the larger fundamental difference in approach between older, traditional methods developed for problems with few parameters, and new methodologies for parameter estimation and uncertainty analysis based on solution of highly parameterized, ill-posed inverse problems that seek to redress the deficiencies of these older methods. Hunt et al. (2007) summarize the underlying issues; they define a ‘‘traditional” approach to model calibration as one which relies on pre-calibration user-intervention to formulate an over-determined (more useful observations than parameters solved for), well-posed (mathematically solvable) inverse problem through pre-emptive parameter simplification. In distributed hydrologic models, traditional parameter simplification is most commonly accomplished using devices such as zones of piecewise constancy, and may be accompanied by the introduction of prior information pertaining to one or a number of elements comprising the simplified parameter set. A nagging concern with these and similar approaches, however, is that the configuration of such zones, and

Fusion of imaging spectrometer and LIDAR data over combined radiative transfer models for forest canopy characterization

A comprehensive canopy characterization of forests is derived from the combined remote sensing signal of imaging spectrometry and large footprint LIDAR. The inversion of two linked physically based Radiative Transfer Models (RTM) provided the platform for synergistically exploiting the specific and independent information dimensions obtained by the two earth observation systems. Due to its measurement principle, LIght Detection And Ranging (LIDAR) is particularly suited to assess the horizontal and vertical canopy structure of forests, while the spectral measurements of imaging spectrometry are specifically rich on information for biophysical and -chemical canopy properties. In the presented approach, the specific information content inherent to the observations of the respective sensor was not only able to complement the canopy characterization, but also helped to solve the ill-posed problem of the RTM inversion. The theoretical feasibility of the proposed earth observation concept has been tested on a synthetic data set generated by a forest growth model for a wide range of forest stands. Robust estimates on forest canopy characteristics were achieved, ranging from maximal tree height, fractional cover (fcover), Leaf Area Index (LAI) to the foliage chlorophyll and water content. The introduction of prior information on the canopy structure derived from large footprint LIDAR observations significantly improved the retrieval performance relative to estimates based solely on spectral information.

A marginal-predictive approach to identifying household parameters

Estimation of household parameters in scanner panel data requires the introduction of prior information. Traditionally, prior information is incorporated by restricting parameters to be constant across households or by specifying a random coefficient distribution. An alternative solution is to incorporate stochastic prior information in a formal Bayesian approach. In standard Bayesian analysis, a prior distribution over the model parameters is specified and combined with the household likelihood to obtain the Bayes estimates. The construction of the prior distribution over model parameters may be difficult, especially when working with new models whose parameters are difficult to interpret. In this paper, we propose a solution which specifies prior information through the marginal distribution of the data, i.e., the outcomes. We evaluate this marginal-predictive approach, using both actual and simulated panel data, and show it to be highly accurate relative to other available alternatives.

An empirical two market disequilibrium model for Dutch manufacturing

We develop an empirical version of the aggregate two market fixprice model due to Barro and Grossman. Maximum likelihood estimation of the model appears to be difficult as a large number of local maxima occurs. The number of local maxima is considerably reduced by the introduction of prior information on some of the standard deviations of the disturbance terms in the model. Satisfactory estimation results are obtained this way. The model closely reproduces the post war Dutch trade cycle, although some problems remain, especially with the seventies.

Collinearity and forecasting

AbstractOld saw: Collinearity will not harm forecasts as long as it continues into the forecast period. If, however, collinearity is unlikely to continue but has already harmed estimation, then corrective action (introduction of prior information) to improve estimates should improve forecasts. This paper marshals the diagnostic information needed to assess collinearity's continuance and, when required, to direct meaningful corrective action. The process is illustrated by an example involving forecasts using energy prices.

Simultaneous spectroscopic determination of several intrinsic profile parameters of a complex spectral line

where K is an operator describing the effect of the apparatus on the observed spectrum, and is the random error in the determination of the ordinate of the observed profile (see, e.g., [I]). In a real situation, when the measurement error is finite, the problem is mathematically improper. As shown in [1-4], it may be solved properly by the introduction of prior information obtained independently.

Use of Prior Information in the Analysis and Estimation of Cobb-Douglas Production Function Models

IN ANALYSES OF PRODUCTION-FUNCTION MODELS employing aggregate time-series data, it is often difficult to obtain precise estimates of parameters because input variables are usually highly intercorrelated. For example, in the case of a CobbDouglas (CD) production function with neutral technical change, the labor, capital, and time-trend variables are frequently highly intercorrelated, a fact that may lead to imprecise parameter estimates and, as will be seen below with U.S. data, highly implausible point estimates for certain parameters. It is generally recognized that introduction of prior information is one possible way of dealing with the above problems-sometimes referred to as multicollinearity problems. Another course of action in dealing with these problems is to extend the data base-say, by combining cross-section data with the available timeseries data. In both approaches, or in a combination of them, additional information is added to the information in the aggregate time-series sample in an attempt to improve the quality of inferences. In the present paper, we review a sampling-theory analysis of the CD production function put forward by Morishima and Saito [3], in which nondata-based prior information was added to aggregate time-series information in order to deal with what appears to be a multicollinearity problem. Then we turn to an analysis of the Morishima-Saito (MS) production function problem, using the. Bayesian approach in which nondata-based prior information is introduced by use of prior probability density functions (pdf's) for the parameters. By the use of prior pdf's in the Bayesian approach, it will be seen that prior information can be introduced in a rather flexible manner, and posterior pdf's for parameters of interest, that reflect both sample and prior information, can be readily computed. In particular, we wish to assess how sensitive inferences about the technical-change parameter and other parameters are to the form and manner in which prior information is introduced. The plan of the paper is as follows. In Section 2, we state the problem and review some sampling-theory results. We then present Bayesian analyses of the problem in Section 3 and provide a summary of results and some concluding remarks in Section 4.