Bayesian model

This paper details the application of a decision tree algorithm for classification of soil types. The productivity of agriculture depends on environmental conditions and soil types. Soil dataset of particular area is downloaded from the Kaggle website for the purpose of finding the classification of soil types and based on the types of soil predicted the agriculturist will sow the seed. In this paper Soil types are classified by applying Bayesian approach to Decision Tree algorithm with Bayesian model is used for finding the classification of soil types. The idea behind is rather simple but powerful. The proposed algorithm of ers some unique features not to be found in any other tree inducers while at the same time it can produce better results for many dif icult problems. Experimental results are presented which illustrate the performance of generating best decision tree for classifying soil type from the given soil dataset. The Algorithm of Bayesian approach to Decision Tree helps to classify the soil types more accurately than the existing Algorithms KNN, SVM and Decision Tree selected for this research paper.

We propose Bayesian inference in hazard regression models where the baseline hazard is unknown, covariate effects are possibly agevarying (non-proportional), and there is multiplicative frailty with arbitrary distribution. Our framework incorporates a wide variety of order restrictions on covariate dependence and duration dependence (ageing). We propose estimation and evaluation of age-varying covariate e¤ects when covariate dependence is monotone rather than proportional. In particular, we consider situations where the lifetime conditional on a higher value of the covariate ages faster or slower than that conditional on a lower value; this kind of situation is common in applications. In addition, there may be restrictions on the nature of ageing. For example, relevant theory may suggest that the baseline hazard function decreases with age. The proposed framework enables evaluation of order restrictions in the nature of both covariate and duration dependence as well as estimation of...

A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the ...

Financial time series analysis deals with the understanding of data collected on financial markets. Several parametric distribution models have been entertained for describing, estimating and predicting the dynamics of financial time series. Alternatively, this article considers a Bayesian semiparametric approach. In particular, the usual parametric distributional assumptions of the GARCH-type models are relaxed by entertaining the class of location-scale mixtures

A standard form of analysis for linguistic typology is the universal implication. These implications state facts about the range of extant languages, such as ``if objects come after verbs, then adjectives come after nouns.'' Such implications are typically discovered by painstaking hand analysis over a small sample of languages. We propose a computational model for assisting at this process. Our model is able to discover both well-known implications as well as some novel implications that deserve further study. Moreover, through a careful application of hierarchical analysis, we are able to cope with the well-known sampling problem: languages are not independent.

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.

We introduce a tractable family of Bayesian generalization functions. The family extends the basic model proposed by Tenenbaum and Griffiths (2001), allowing richer variation in sampling assumptions and prior beliefs. We derive analytic expressions for these generalization functions, and provide an explicit model for experimental data. We then present an experiment that tests the basic model predictions within the core domain of the theory, namely tasks that require people to make inductive judgments about whether some property holds for novel items. Analysis of the results illustrates the importance of describing variations in people’s prior beliefs and assumptions about how items are sampled and of having an explicit model for the entire task.

In MCMC methods, such as the Metropolis-Hastings (MH) algorithm, the Gibbs sampler, or recent adaptive methods, many different strategies can be proposed, often asso-ciated in practice to unknown rates of convergence. In this paper we propose a simulation-based methodology to compare these rates of convergence, grounded on an entropy criterion computed from parallel (i.i.d.) simulated Markov chains coming from each candidate strat-egy. Our criterion determines the most efficient strategy among the candidates. Theoreti-cally, we give for the MH algorithm general conditions under which its successive densities satisfy adequate smoothness and tail properties, so that this entropy criterion can be esti-mated consistently using kernel density estimate and Monte Carlo integration. Simulated and actual examples in moderate dimensions are provided to illustrate this approach.

A detailed dendrochronological survey was performed on the medieval roofs of the Church of Our Lady (CoOL) in Damme, Belgium. Seen its complex architectural history, special attention was paid to the identification of consecutive building phases, based on combined architectural historical research and tree-ring dating. In total 64 increment cores were taken throughout the roof structures of the CoOL. All roof timbers are made of European oak (Quercus robur/petraea), of which only few have surviving sapwood or bark. Tree-ring dating confirms the late 13th/early 14th century construction date of the roofs. For all chronologies that were composed, the highest correlation values are found with reference chronologies covering the catchment area of the river Meuse. From the dating results of the timbers of the CoOL it becomes clear that the same timber source was used for nearly a century. On several of the examined roof timbers, rafting joints were observed, demonstrating that the timbers were indeed tied together as a raft and floated down the river.By implementing sapwood estimates in a Bayesian chronological model (OxCal), tree-ring series with surviving sapwood from coeval roof structures were combined in order to narrow down the time range for the felling date. Based on the refined interpretation of the felling dates, several consecutive building phases can now be identified and dated, leading to a new interpretation of the architectural history of the CoOL. Intriguingly, a marked interruption in building activities is observed around 1300. Probably this is related to the instable political situation at that time, caused by the armed conflict that emerged between the Count of Flanders and the king of France. Since Damme served as the outport of the riotous city of Bruges, it was alternately seized by the French and Flemish, both consuming considerable amounts of timber and other building materials for military fortifications. Potentially this led to a shortage in building materials and provoked a stop in building activities.This paper demonstrates the power of Bayesian models to refine the interpretation of dendrochronological dates in architectural analyses of medieval historical buildings.

Practitioners and construction management researchers lack believable and practical methods to assess the value proposition of emerging methods such as Virtual Design and Construction (VDC) including understanding how different levels of implementation affect its benefits. Furthermore, current methods of understanding VDC implementation and benefits cannot be updated easily to incorporate new data. This paper presents a Bayesian framework to predict benefits from application of Virtual Design and Construction (VDC) given data about its implementation. We analyzed data from 40 projects that performed some formal modeling of the project scope and/or the construction process. The analysis suggests that more extensive or higher levels of VDC implementation lead to higher project benefits. We explain the use of a Bayesian framework as an alternative to the application of classical probability theory to construction management research, how we used it to interpret data about VDC practice and outcomes, our finding that benefits have strong positive contingent correlation with the level of VDC implemented on projects, and our suggestion to use the method to update conclusions about benefits given changing data about implementation and outcomes.