Probabilistic Model Selection Research Articles

Prediction and Analysis of House Price Through Machine Learning Approach

In recent years, picture detection, spam restructuring, natural speech command, product recommendation, and medical diagnosis have all benefited greatly from machine learning. The current machine learning algorithm aids in improving medical advancements, public safety, and security alerts. In the current work, we talk about the machine learning algorithm-generated forecast of future home prices. We compare and investigate numerous prediction methods for the selection of prediction methods. We employ lasso regression as our model because to its flexible and probabilistic model selection process We construct a housing cost prediction model in the considering such machine learning algorithm models as XG Boost, lasso regression, and neural systems on look at their order precision execution. At that point, we urge a real estate agent or home seller to employ a housing cost estimates model to assist them with better information based on property valuation. In regards to precision those tests show that the lasso regression algorithm constantly beats alternative models when utilized to predict costs associated with housing.

Physics-based probabilistic capacity models and fragility estimates for light wood frame shear walls

Light wood frame buildings are common in the United States, and due to their prevalence, there has been significant damage to this type of building in previous earthquakes. For this building type, shear walls (typically sheathed with oriented strand board) are the main lateral force-resisting element. Probabilistic models and fragility estimates incorporating the prevailing uncertainties for shear walls are needed to make accurate predictions of the effects of future earthquakes on buildings of this type. This paper uses experimental data to develop probabilistic capacity models for the shear force and drift of light wood frame shear walls at the yield, peak, and ultimate performance levels. The proposed models are applicable to shear walls in low-rise buildings where the shear deformation is dominant. Moreover, the proposed models are applicable to walls that are similar to walls in the dataset. The probabilistic models are constructed starting from deterministic models. For the shear force, a new deterministic model is proposed. This model predicts the shear force capacity of the wall based on the capacity of individual nails. For drift, existing deterministic drift limits are used. The deterministic models are then improved by incorporating the effect of additional physical characteristics through explanatory basis functions that are shown to be significant in a probabilistic model selection procedure. Finally, fragility estimates are constructed for an example shear wall using the proposed capacity models and conditioning on the shear force and drift demands.

Fundamental limits to learning closed-form mathematical models from data

Given a finite and noisy dataset generated with a closed-form mathematical model, when is it possible to learn the true generating model from the data alone? This is the question we investigate here. We show that this model-learning problem displays a transition from a low-noise phase in which the true model can be learned, to a phase in which the observation noise is too high for the true model to be learned by any method. Both in the low-noise phase and in the high-noise phase, probabilistic model selection leads to optimal generalization to unseen data. This is in contrast to standard machine learning approaches, including artificial neural networks, which in this particular problem are limited, in the low-noise phase, by their ability to interpolate. In the transition region between the learnable and unlearnable phases, generalization is hard for all approaches including probabilistic model selection.

Machine Learning Based House Price Prediction Using Modified Extreme Boosting

In recent years, machine learning has become increasingly important in everyday voice commands and predictions. Instead, it provides a safer auto system and better customer assistance. As a result of all that has been demonstrated, ML is a technology that is becoming more and more popular in a range of industries. To gauge changes in house values, the House Price Index is frequently employed (HPI). Due to the substantial correlation that exists between property prices and other variables, such as location, region, and population, the HPI on its own is not sufficient to accurately forecast a person's house price. Some studies have successfully predicted house prices using conventional machine learning techniques, but they seldom evaluate the efficacy of different models and ignore the more complicated but less well-known models. We proposed Modified Extreme Gradient Boosting as our model in this study due to its adaptive and probabilistic model selection process. Feature engineering, hyperparameter training and optimization, model interpretation, and model selection and evaluation are all steps in the process. Home price indices, which are frequently used to support real estate policy initiatives and estimate housing costs. In this project, models for forecasting changes in home prices are developed using machine learning methods.

Observer Design for an Inertia Wheel Pendulum with Static Friction

A state observer design for the inertia wheel pendulum considering static friction of the actuated inertia disc is presented. The frictional force is modeled by the Stribeck effect, with two separate differential equations describing the sticking and non-sticking system, respectively. The transition between the two scenarios is in general determined by a static friction condition. Three proposed observers are designed for both the sticking and non-sticking model. Depending on the probability of the respective model given a current measurement, the more likely is selected for state estimation. The performance is demonstrated on a laboratory demonstrator, where the observers including the proposed probabilistic model selection are compared.

Latitude does not influence cavity entrance orientation of South American avian excavators

AbstractIn the Northern Hemisphere, several avian cavity excavators (e.g., woodpeckers) orient their cavities increasingly toward the equator as latitude increases (i.e. farther north), and it is proposed that they do so to take advantage of incident solar radiation at their nests. If latitude is a key driver of cavity orientations globally, this pattern should extend to the Southern Hemisphere. Here, we test the prediction that cavities are oriented increasingly northward at higher (i.e. colder) latitudes in the Southern Hemisphere and describe the preferred entrance direction(s) of 1,501 cavities excavated by 25 avian species (n = 22 Picidae, 2 Trogonidae, 1 Furnariidae) across 12 terrestrial ecoregions (15°S to 55°S) in South America. We used Bayesian projected normal mixed-effects models for circular data to examine the influence of latitude, and potential confounding factors, on cavity orientation. Also, a probability model-selection procedure was used to simultaneously examine multiple orientation hypotheses in each ecoregion to explore underlying cavity-orientation patterns. Contrary to predictions, and patterns from the Northern Hemisphere, birds did not orient their cavities more toward the equator with increasing latitude, suggesting that latitude may not be an important underlying selective force shaping excavation behavior in South America. Moreover, unimodal cavity-entrance orientations were not frequent among the ecoregions analyzed (only in 4 ecoregions), whereas bimodal (in 5 ecoregions) or uniform (in 3 ecoregions) orientations were also present, although many of these patterns were not very clear. Our results highlight the need to include data from under-studied biotas and regions to improve inferences at macroecological scales. Furthermore, we suggest a re-analysis of Northern Hemisphere cavity orientation patterns using a multi-model approach, and a more comprehensive assessment of the role of environmental factors as drivers of cavity orientation at different spatial scales in both hemispheres.

A systematic comparison of statistical and hydrological methods for design flood estimation

Abstract We compare statistical and hydrological methods to estimate design floods by proposing a framework that is based on assuming a synthetic scenario considered as ‘truth’ and use it as a benchmark for analysing results. To illustrate the framework, we used probability model selection and model averaging as statistical methods, while continuous simulations made with a simple and relatively complex rainfall–runoff model are used as hydrological methods. The results of our numerical exercise show that design floods estimated by using a simple rainfall–runoff model have small parameter uncertainty and limited errors, even for high return periods. Statistical methods perform better than the linear reservoir model in terms of median errors for high return periods, but their uncertainty (i.e., variance of the error) is larger. Moreover, selecting the best fitting probability distribution is associated with numerous outliers. On the contrary, using multiple probability distributions, regardless of their capability in fitting the data, leads to significantly fewer outliers, while keeping a similar accuracy. Thus, we find that, among the statistical methods, model averaging is a better option than model selection. Our results also show the relevance of the precautionary principle in design flood estimation, and thus help develop general recommendations for practitioners and experts involved in flood risk reduction.

Data-based bivariate uncertainty assessment of extreme rainfall-runoff using copulas: comparison between annual maximum series (AMS) and peaks over threshold (POT).

Bivariate frequency analysis of extreme rainfall and runoff is crucial for water resource planning and management in a river basin. This study is aimed at accounting for uncertainties in bivariate analysis of extreme rainfall-runoff frequency in the Taleghan watershed, one of the major watersheds in northern Iran, using copulas. Two types of paired rainfall and runoff data, including annual maximum series (AMS) and peaks over threshold (POT) are adopted to investigate the uncertainties that arose due to the input data. The Cramer von-Mises goodness-of-fit test and Akaike information criteria (AIC) reveal that the Student's t copula is the best-fit copula for PAMS-QAMS with Gaussian-Pearson III (P3) margins, while the Plackett copula is the best-fit copula for PPOT-QPOT with generalized Pareto (GPAR-GPAR) margins. A nonparametric bootstrapping method for sampling from p-level curves is established to investigate the effects of univariate and bivariate models selection and uncertainties induced by input data. The results indicated that the sampling uncertainty reduces POT data compared to AMS data due to the increased sample size. However, the parameterization uncertainty of the POT data increases because of the weaker dependence structure between rainfall and runoff for the POT data. The results also reveal that the larger sampling uncertainties are associated with higher p-level curves for both AMS and POT data which are induced by lower data density in the upper tail. For the study area, the input-data uncertainty is most significant in bivariate rainfall-runoff frequency analysis and quantile estimation, while the uncertainty induced by probabilistic model selection is least significant.

Dynamic Scoring: Probabilistic Model Selection Based on Utility Maximization.

We propose a novel approach of model selection for probability estimates that may be applied in time evolving setting. Specifically, we show that any discrepancy between different probability estimates opens a possibility to compare them by trading on a hypothetical betting market that trades probabilities. We describe the mechanism of such a market, where agents maximize some utility function which determines the optimal trading volume for given odds. This procedure produces supply and demand functions, that determine the size of the bet as a function of a trading probability. These functions are closed form for the choice of logarithmic and exponential utility functions. Having two probability estimates and the corresponding supply and demand functions, the trade matching these estimates happens at the intersection of the supply and demand functions. We show that an agent using correct probabilities will realize a profit in expectation when trading against any other set of probabilities. The expected profit realized by the correct view of the market probabilities can be used as a measure of information in terms of statistical divergence.

Bayesian model selection in synthetic biology: factor levels and observation functions

Data-driven inference of the most plausible mechanistic model within a set of candidates is a major hurdle in synthetic and systems biology. Probabilistic model selection is hampered by limitations in the quality and amount of biological data. Furthermore, the computational cost of discriminating between competing models often leads the user to skip model selection and subjectively choose a model.To challenge this practice, here we took a genetic toggle switch built in E. coli, considered three alternative models of it and used a Bayesian approach to rank these models based on the evidence from in vivo data. As the ranking depends on the information content of the data, we use Bayesian optimisation to design maximally informative inputs, i.e. chemical stimuli for the cells. We then explore how the optimality of such stimuli depended on the degrees of freedom in the optimisation (i.e. the number of segments in the input), showing a decrease of the attainable discriminatory power with the dynamic properties of the perturbation. We finally investigated the effect that the observable(s) selected in the optimisation exerts on the outcome of the latter. Our results suggest that Bayesian optimisation-based experimental design can be adopted as a means to discriminate between competing models of a gene regulatory network.

Dynamic Scoring: Probabilistic Model Selection Based on Utility Maximization

We propose a novel approach of model selection for probability estimates that can be applied in time evolving setting. The idea is that any discrepancy between different probability estimates opens a possibility to compare them by trading on a hypothetical betting market that trades probabilities. We describe the mechanism of such a market, where agents are maximizing some utility function which determines the optimal trading volume for given odds. This procedure produces supply and demand functions, the size of the bet as a function of a trading probability. These functions are analytical for the choice of logarithmic and exponential utility functions. Having two probability estimates and the corresponding supply and demand functions, the trade matching these estimates happens at the intersection of the supply and demand functions. We show that an agent using correct probabilities will realize a profit in expectation when trading against any other set of probabilities.

A probabilistic model selection criterion for spectral clustering

A probabilistic model selection criterion for spectral clustering

Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach

Reliable prediction of short-term passenger flow could greatly support metro authorities’ decision processes, help passengers to adjust their travel schedule, or, in extreme cases, assist emergency management. The inflow and outflow of the metro station are strongly associated with the travel demand within metro networks. The purpose of this paper is to obtain such prediction. We first collect the origin-destination information from the smart-card data and explore the passenger flow patterns in a metro system. We then propose a data driven framework for short-term metro passenger flow prediction with the ability to utilize both spatial and temporal related information. The approach adopts two forecasts as basic models and then uses a probabilistic model selection method, random forest classification, to combine the two outputs to achieve a better forecast. In the experiments, we compare the proposed model with four other prediction models, i.e., autoregressive-moving-average, neural networks, support vector regression, and averaging ensemble model, as well as the basic models. The results indicate that the proposed approach outperforms the others in most cases. The origin-destination flows extracted from smart-card data can be successfully exploited to describe different metro travel patterns. And the framework proposed here, especially the probabilistic combination method, can improve the performance of short-term transportation prediction.

Distribution modeling for reliability analysis: Impact of multiple dependences and probability model selection

• Vine structured copula model is used to represent multivariate distributions. • Copula parameters are determined from correlations by moment matching technique. • The need for accurate dependence characterization is demonstrated in multi-dimensions. • A method of selecting the optimal dependence and distribution model is provided. Reliability analysis requires modeling of joint probability distribution of uncertain parameters, which can be a challenge since the random variables representing the parameter uncertainties may be correlated. For convenience, a Gaussian data dependence is commonly assumed for correlated random variables. This paper first investigates the effect of multidimensional non-Gaussian data dependences underlying the multivariate probability distribution on reliability results. Using different bivariate copulas in a vine structure, various data dependences can be modeled. The associated copula parameters are identified from available statistical information by moment matching techniques. After the development of the vine copula model for representing the multivariate probability distribution, the reliability involving correlated random variables is evaluated based on the Rosenblatt transformation. The impact of data dependence is significant because a large deviation in failure probability is observed, which emphasizes the need for accurate dependence characterization. A practical method for dependence modeling based on limited data is thus provided. The result demonstrates that the non-Gaussian data dependences can be real in practice, and the reliability can be biased if the Gaussian dependence is used inappropriately. Moreover, the effect of conditioning order on reliability should not be overlooked except that the vine structure contains only one type of copula.

Leaf classification in sunflower crops by computer vision and neural networks

In this article, we present an automatic leaves image classification system for sunflower crops using neural networks, which could be used in selective herbicide applications. The system is comprised of four main stages. First, a segmentation based on rgb color space is performed. Second, many different features are detected and then extracted from the segmented image. Third, the most discriminable set of features are selected. Finally, the Generalized Softmax Perceptron (GSP) neural network architecture is used in conjunction with the recently proposed Posterior Probability Model Selection (PPMS) algorithm for complexity selection in order to select the leaves in an image and then classify them either as sunflower or non-sunflower. The experimental results show that the proposed system achieves a high level of accuracy with only five selected discriminative features obtaining an average Correct Classification Rate of 85% and an area under the receiver operation curve over 90%, for the test set.

A Radius and Ulna TW3 Bone Age Assessment System

An end-to-end system to automate the well-known Tanner--Whitehouse (TW3) clinical procedure to estimate the skeletal age in childhood is proposed. The system comprises the detailed analysis of the two most important bones in TW3: the radius and ulna wrist bones. First, a modified version of an adaptive clustering segmentation algorithm is presented to properly semi-automatically segment the contour of the bones. Second, up to 89 features are defined and extracted from bone contours and gray scale information inside the contour, followed by some well-founded feature selection mathematical criteria, based on the ideas of maximizing the classes' separability. Third, bone age is estimated with the help of a Generalized Softmax Perceptron (GSP) neural network (NN) that, after supervised learning and optimal complexity estimation via the application of the recently developed Posterior Probability Model Selection (PPMS) algorithm, is able to accurately predict the different development stages in both radius and ulna from which and with the help of the TW3 methodology, we are able to conveniently score and estimate the bone age of a patient in years, in what can be understood as a multiple-class (multiple stages) pattern recognition approach with posterior probability estimation. Finally, numerical results are presented to evaluate the system performance in predicting the bone stages and the final patient bone age over a private hand image database, with the help of the pediatricians and the radiologists expert diagnoses.

Pruned Resampling: Probabilistic Model Selection Schemes for Sequential Face Recognition

This paper proposes probabilistic pruning techniques for a Bayesian video face recognition system. The system selects the most probable face model using model posterior distributions, which can be calculated using a Sequential Monte Carlo (SMC) method. A combination of two new pruning schemes at the resampling stage significantly boosts computational efficiency by comparison with the original online learning algorithm. Experimental results demonstrate that this approach achieves better performance in terms of both processing time and ID error rate than a contrasting approach with a temporal decay scheme.

Simulation-based forecasting effects of an accidental explosion on the road. Part I: methodological framework

Abstract Forecasting mechanical actions induced by accidental explosions on the road is of crucial importance to assessing potential damage to structures and non‐structural property exposed to them. A logical result of such forecasting may be expressed in the form of probabilistic models. They should quantify likelihood of occurrence and physical characteristics of accidental explosions. Generally the models are to be selected under the conditions of sparse statistical information on intensities and likelihood of explosive actions. The first part of the present paper proposes a simulation‐based procedure intended for selection of the probabilistic models in the absence of direct statistical data on the explosive actions. The proposed procedure is formulated in the context of the classical Bayesian approach to risk assessment. The main idea of the procedure is that statistical samples necessary for fitting the probabilistic models can be acquired from a stochastic simulation of an accident involving an exp...

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectifi...

Estimating Entropy Rates with Bayesian Confidence Intervals

The entropy rate quantifies the amount of uncertainty or disorder produced by any dynamical system. In a spiking neuron, this uncertainty translates into the amount of information potentially encoded and thus the subject of intense theoretical and experimental investigation. Estimating this quantity in observed, experimental data is difficult and requires a judicious selection of probabilistic models, balancing between two opposing biases. We use a model weighting principle originally developed for lossless data compression, following the minimum description length principle. This weighting yields a direct estimator of the entropy rate, which, compared to existing methods, exhibits significantly less bias and converges faster in simulation. With Monte Carlo techinques, we estimate a Bayesian confidence interval for the entropy rate. In related work, we apply these ideas to estimate the information rates between sensory stimuli and neural responses in experimental data (Shlens, Kennel, Abarbanel, & Chichilnisky, in preparation).