Bayesian Methods (MCMC)

The stochastic or random nature of commodity prices plays a central role in models for valuing financial contingent claims on commodities. In this paper, by enhancing a multi factor framework which is consistent not only with the market observable forward price curve but also the volatilities and correlations of forward prices, we propose a two factor stochastic volatility model for the evolution of the gas forward curve. The volatility is stochastic due to a hidden Markov Chain that causes it to switch between "on peak" and "off peak" states. Based on the structure functional forms for the volatility, we propose and implement the Markov Chain Monte Carlo (MCMC) method to estimate the parameters of the forward curve model. Applications to simulated data indicate that the proposed algorithm is able to accommodate more general features, such as regime switching and seasonality. Applications to the market gas forward data shows that the MCMC approach provides stable estimates.

We investigate the use of the Riemannianoptimization method over the flag manifold in subspace ICA problems such as in-dependent subspace analysis (ISA) and complex ICA. In the ISA experiment, we use the Riemannian approach over the flag manifold together ...

1] One challenge that faces hydrologists in water resources planning is to predict the catchment's response to a given rainfall. Estimation of parameter uncertainty (and model uncertainty) allows assessment of the risk in likely applications of hydrological models. Bayesian statistical inference provides an ideal means of assessing parameter uncertainty, whereby prior knowledge about the parameter is combined with information from the available data to produce a probability distribution (the posterior distribution) that describes uncertainty about the parameter and serves as a basis for selecting appropriate values for use in modeling applications. Widespread use of Bayesian techniques in hydrology has been hindered by difficulties in summarizing and exploring the posterior distribution. These difficulties have been largely overcome by recent advances in Markov chain Monte Carlo (MCMC) methods that involve Monte Carlo sampling of the posterior distribution. This study compares four MCMC sampling algorithms in the context of rainfall-runoff modeling. The algorithms compared include a conventional Metropolis-Hastings algorithm used previously in hydrological applications which uses a combination of block and single-site updating and an adaptive Metropolis algorithm that has characteristics that are well suited to model parameters with a high degree of correlation and interdependence, as is often evident in hydrological models. In addition to these, two other algorithms are evaluated to clarify the relative importance of updating all model parameters as a block versus updating each parameter one at a time. The MCMC techniques are compared for simplicity, ease of use, statistical efficiency in exploration of the parameter space, and speed of implementation, using 11 years of daily rainfall-runoff data from the Bass river catchment in Australia. The results show that the adaptive Metropolis algorithm is superior in many respects and can offer a relatively simple basis for assessing parameter uncertainty in hydrological modeling studies and that the efficiency of the adaptive algorithm is not solely attributed to the blockupdating element of the algorithm.

In this paper we present a robust vision-based system for vehicle tracking and classification devised for traffic flow surveillance. The system performs in real time achieving good results even in challenging situations, such as with moving casted shadows on sunny days, headlight reflections on the road, rainy days and traffic jams, using only a single standard camera. We propose a robust adaptive multi-cue segmentation strategy that detects foreground pixels corresponding to moving and stopped vehicles, even with noisy images due to compression. First, the approach adaptively thresholds a combination of luminance and chromaticity disparity maps between the learned background and the current frame. It then adds extra features derived from gradient differences, in order to improve the segmentation of dark vehicles with casted shadows, and removes headlights reflections on the road. The segmentation is further used by a two-step tracking approach, which combines the simplicity of a linear 2D Kalman filter, and the complexity of a 3D volume estimation using Markov Chain Monte Carlo (MCMC) methods. Experimental results show that our method can count and classify vehicles in real time with a high level of performance under different environmental situations, comparable to those of inductive loop detectors (ILD).

En este documento se presenta la versión inicial 1.0, aún en desarrollo, de un sistema abierto escrito en TOL1 para la simulación e inferencia bayesianas de tipo MonteCarlo-Markov Chain (MCMC) mediante el algoritmo de Gibbs, sobre modelos de regresión lineal sparse con estructura arbitraria (jerárquicos, redes bayesianas, ...) con restricciones de desigualdad lineal, con tratamiento de omitidos y filtros no lineales, ambas tanto en el input como en el output, así como con estructuras ARIMA.

We describe a sequential importance sampling (SIS) procedure for analyzing two-way zero-one or contingency tables with fixed marginal sums. An essential feature of the new method is that it samples the columns of the table progressively according to certain special distributions. Our method produces Monte Carlo samples that are remarkably close to the uniform distribution, enabling one to approximate closely the null distributions of various test statistics about these tables. Our method compares favorably with other existing Monte Carlobased algorithms, and sometimes is a few orders of magnitude more efficient. In particular, compared with Markov chain Monte Carlo (MCMC)-based approaches, our importance sampling method not only is more efficient in terms of absolute running time and frees one from pondering over the mixing issue, but also provides an easy and accurate estimate of the total number of tables with fixed marginal sums, which is far more difficult for an MCMC method to achieve.

The stochastic or random nature of commodity prices plays a central role in models for valuing financial contingent claims on commodities. In this paper, by enhancing a multifactor framework which is consistent not only with the market observable forward price curve but also the volatilities and correlations of forward prices, we propose a two factor stochastic volatility model for the evolution of the gas forward curve. The volatility is stochastic due to a hidden Markov Chain that causes it to switch between "on peak" and "off peak" states. Based on the structure functional forms for the volatility, we propose and implement the Markov Chain Monte Carlo (MCMC) method to estimate the parameters of the forward curve model. Applications to simulated data indicate that the proposed algorithm is able to accommodate more general features, such as regime switching and seasonality. Applications to the market gas forward data shows that the MCMC approach provides stable ...

A novel semiparametric regression model for censored data is proposed as an alternative to the widely used proportional hazards survival model. The proposed regression model for censored data turns out to be flexible and practically meaningful. Features include physical interpretation of the regression coefficients through the mean response time instead of the hazard functions, and a rigorous proof of consistency of the posterior distribution. It is shown that the regression model obtained by a mixture of parametric families, has a proportional mean structure (as in an accelerated failure time models). The statistical inference is based on a nonparametric Bayesian approach that uses a Dirichlet process prior for the mixing distribution. Consistency of the posterior distribution of the regression parameters in the Euclidean metric is established. Finite sample parameter estimates along with associated measure of uncertainties can be computed by a MCMC method. Simulation studies are p...

Multilevel structural equation modeling (MSEM) is gaining popularity in the social sciences as a framework for estimating latent variable models in the presence of hierarchical data. In addition, we believe that MSEMs are quite helpful to psychological and educational inquiries. Many research papers have been published on technical developments in MSEM; however, we are not aware of any tutorials on how to properly implement these complex models. In addition, applied researchers may be unfamiliar with how to implement a Bayesian estimation approach to MSEM, despite the fact that it has distinct advantages in this modeling context (Depaoli and Clifton, 2015; Hox, van de Schoot, and Matthijsse, 2012). To that end, this paper serves as a nontechnical tutorial on the implementation and application of MSEM using both frequentist and Bayesian estimation methods. We demonstrate the implementation of MSEM with three motivating examples using data from the Program for International Student Assessment (PISA). In addition, we present a small Monte Carlo study to show how the estimation of MSEM is impacted by different types of priors for data structures similar to those presented in the PISA examples. We conclude with recommendations for applied researchers based on findings from the empirical examples and the simulation study. We also outline future methodological considerations in the context of Bayesian MSEM.

This work presents the current state-of-the-art in techniques for tracking a number of objects moving in a coordinated and interacting fashion. Groups are structured objects  characterized with particular motion patterns. The group can be comprised of a small number of interacting objects (e.g. pedestrians, sport players, convoy of cars) or of hundreds or thousands of components such as crowds of people. The
group object tracking is closely linked with extended object tracking but at the same time has particular features which differentiate it from extended objects. Extended objects, such as in maritime surveillance, are characterized by their kinematic states and their size or volume. Both group and extended objects give rise to a varying number of measurements and require trajectory maintenance. An emphasis is given here to sequential Monte Carlo (SMC) methods and their variants. Methods for small groups and for
large groups are presented, including Markov Chain Monte Carlo (MCMC) methods, the random matrices approach and Random Finite Set Statistics methods. Efficient real-time implementations are discussed which are able to deal with the high dimensionality and provide high accuracy. Future trends and avenues are traced.

This paper describes the implementation and performance of PBPI, a parallel implementation of Bayesian phylogenetic inference method for DNA sequence data. By combining the Markov Chain Monte Carlo (MCMC) method with likelihood-based assessment of phylogenies, Bayesian phylogenetic inferences can incorporate complex statistic models into the process of phylogenetic tree estimation. However, Bayesian analyses are extremely computationally expensive. PBPI uses algorithmic improvements and parallel processing to achieve significant performance improvement over comparable Bayesian phylogenetic inference programs. We evaluated the performance and accuracy of PBPI using a simulated dataset on System X, a terascale supercomputer at Virginia Tech. Our results show that PBPI identifies equivalent tree estimates 1424 times faster on 256 processors than a widely-used, best-available (albeit sequential), Bayesian phylogenetic inference program. PBPI also achieves linear speedup with the number of processors for large problem sizes. Most importantly, the PBPI framework enables Bayesian phylogenetic analysis of large datasets previously impracticable.

This paper outlines a new methodological framework for combining indicators of corruption. The state-space framework extends the methodology of the Worldwide Governance Indicators (WGI) to fully make use of the time-structure present in corruption data. It is estimated using a Bayesian Gibbs sampler algorithm.

The state-space framework holds many advantages from a practical, an estimation and a theoretical point of view. Most importantly, it significantly expands the period for which the index can be computed while at the same time addressing the selection bias issues that trouble the Corruption Perceptions Index (CPI). In addition, its estimates are more stable and have smaller confidence intervals than both CPI and WGI. Because the estimation is transparent and data is entered without any manipulations, the estimation procedure is more objective.

In this thesis, we address several problems related to modelling complex systems. The difficulty of modelling complex systems lies partly in their topology and how they form rather complex networks. From this perspective, our interest in networks (graphs) is part of a broader current of research on complex systems.
Graphical models provide powerful tools to model and make the statistical inference regarding complex relationships among variables. In this context, Gaussian graphical models are commonly used, since inference in such models is often tractable. In Chapter 2, we introduce a novel Bayesian framework for Gaussian graphical model determination.
We carry out the posterior inference by using an efficient sampling scheme which is a trans-dimensional MCMC approach based on birth-death process. In particular, we construct an efficient search algorithm which explores the graph space to detect the underlying graph with high accuracy. We cover the theory and computational details of the proposed method. We then apply the method to large-scale real applications from mammary gene expression studies to show its empirical usefulness.

Water scarcity due to climate change as well as inappropriate water governance is one of the important topics in the world, particularly in developing countries. Most people who live close to the water resource are not always economically-advantaged. Moreover, it might be seems that people living close to "economically-attractive water resource" are classified into lower category according to the standard of living in the country. Community based water supply system is one of strong alternatives to existing water supply system by the public sector. The community based water supply system works more effectively if it is based upon strong community network in the region. This study conducts an empirical research on community based water supply system in Indonesian rural area. In this paper, we propose a discrete-choice model which describes the mechanism of resident's spontaneous collaboration to access water. We formulate a hypothesis that households with better community tie have ability to organize "community based" management system. In order to test it, we formulate a spatial probit model which can consider the effect of social interaction upon their choices in water supply system. Traditionally, spatial models are estimated with maximum likelihood method, however, in this paper, we adopt Markov chain Monte Carlo (MCMC) method to estimate parameters due to the difficulty in estimation of discrete-choice model with spatial interaction term. Using dataset from a field survey in Indonesia which we conducted in 2008 the spatial probit model is empirically tested to show that social interaction in the community plays an important role on resident's spontaneous collaboration to manage community-based water supply system.

The reaction kinetics of neopentyl glycol esterification with propionic acid is studied in a batch reactor at different temperatures. The reaction is catalyzed by a heterogeneous resin (DOWEX 50 WX 2). The parameters of the kinetic model are estimated by non-linear regression by minimizing the difference between the measured and predicted concentrations. The reliability of the estimated parameters is analyzed using the Markov chain Monte Carlo (MCMC) method. The MCMC method is an advanced statistical approach, which is well suited for the analysis of complex multiparametric non-linear models. The potential of the MCMC method is further demonstrated by optimizing the product yield in CSTR using the estimated parameters from the batch experiments. ᭧

This paper demonstrates Markov chain Monte Carlo (MCMC) techniques that are particularly well-suited to complex models with item response theory (IRT) assumptions. MCMC may be thought of as a successor to the standard practice of rst calibrating the items using E-M methods and then taking the item parameters to be known and xed at their calibrated values when proceeding with inference regarding the latent trait. In contrast to this two-stage E-M approach, MCMC methods treat item and subject parameters at the same time; this allows us to incorporate standard errors of item estimates into trait inferences, and vice versa. We develop a MCMC methodology based on Metropolis-Hastings sampling, that can be routinely implemented to t novel IRT models, and compare the algorithmic features of the Metropolis-Hastings approach to other approaches based on Gibbs sampling. For concreteness we illustrate the methodology using the familiar two-parameter logistic (2PL) IRT model; more complex models are treated in a subsequent paper .

In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volatility model with leverage eect, non constant conditional mean and jumps. Our idea relies on the auxiliary particle lter algorithm together with the Markov Chain Monte Carlo (MCMC) method- ology. Our method allows to sequentially evaluate the parameters and the latent processes involved in the dynamic of interest. An empirical applica- tion on simulated data and on the Standard & Poor's 500 index is presented to study the performance of the algorithm implemented.

Let P(E) be the space of probability measures on a measurable space (E, E). In this paper we introduce a class of non-linear Markov Chain Monte Carlo (MCMC) methods for simulating from a probability measure π ∈ P(E). Non-linear Markov kernels (e.g. Del ; Del ) K : P(E) × E → P(E) can be constructed to admit π as an invariant distribution and have superior mixing properties to ordinary (linear) MCMC kernels. However, such non-linear kernels cannot be simulated exactly, so, in the spirit of particle approximations of Feynman-Kac formulae (Del Moral 2004), we construct approximations of the non-linear kernels via Self-Interacting Markov Chains (Del Moral & Miclo 2004) (SIMC). We present several non-linear kernels and demonstrate that, under some conditions, the associated self-interacting approximations exhibit a strong law of large numbers; our proof technique is via the Poisson equation and Foster-Lyapunov conditions. We investigate the performance of our approximations with some simulations, combining the methodology with population-based Markov chain Monte Carlo (e.g. Jasra et al. ). We also provide a comparison of our methods with sequential Monte Carlo samplers when applied to a continuous-time stochastic volatility model.

Dynamic survival models are a useful extension of the popular Cox model as the effects of explanatory variables are allowed to change over time. In this paper a new auxiliary mixture sampler for Bayesian estimation of the model parameters is introduced. This sampler forms the basis of a model space MCMC method for stochastic model specification search in dynamic survival models, which involves selection of covariates to include in the model as well as specification of effects as time-varying or constant. The method is applied to two well-known data sets from the literature.

Quantitative individual human diet reconstruction using isotopic data and a Bayesian approach typically requires the inclusion of several model parameters, such as individual isotopic data, isotopic and macronutrient composition of food groups, diet-to-tissue isotopic offsets and dietary routing. In an archaeological context, sparse data may hamper a widespread application of such models. However, simpler models may be proposed to address specific archaeological questions. As a consequence of the intake of marine foods, individuals from the first century AD Roman site of Herculaneum showed well-defined bone collagen radiocarbon
age offsets from the expected terrestrial value. Taking as reference these radiocarbon offsets and using as model input stable isotope data (δ13C and δ15N), the performance of two Bayesian mixing model instances (routed and concentration dependent model versus nonrouted and concentration-independent) was compared to predict the carbon contribution of marine foods to bone collagen. Predictions generated by both models were in good agreement with observed values. The model with higher complexity showed only a slightly better performance in terms of accuracy and precision. This demonstrates that under similar circumstances, a simple Bayesian approach can be applied to quantify the carbon contribution of marine foods to human bone collagen.

The book about the eventology ~ a science about events and its applications to problems of management of sets of events ~ a new direction in philosophy, mathematics and event management (see English version of the book: https://www.academia.edu/38605343/).
This book outlines ideas that inspired me while I was trying to build a new event science, eventology. Not each of these ideas was developed, but all of them inspired me in the construction of eventology, and then in the discovery of a revolutionary theory of experience and chance, co~eventum mechanics. (see, for example, https://www.academia.edu/34357251,
https://www.academia.edu/34373279,
https://www.academia.edu/34390291,
https://www.academia.edu/38368776,
https://www.academia.edu/38154263 and so on).

In some clinical trials and epidemiologic studies, investigators are interested in knowing whether the variability of a biomarker is independently predictive of clinical outcomes. This question is often addressed via a naïve approach where a sample-based estimate (e.g., standard deviation) is calculated as a surrogate for the "true" variability and then used in regression models as a covariate assumed to be free of measurement error. However, it is well known that the measurement error in covariates causes underestimation of the true association. The issue of underestimation can be substantial when the precision is low because of limited number of measures per subject. The joint analysis of survival data and longitudinal data enables one to account for the measurement error in longitudinal data and has received substantial attention in recent years. In this paper we propose a joint model to assess the predictive effect of biomarker variability. The joint model consists of two linked sub-models, a linear mixed model with patient-specific variance for longitudinal data and a full parametric Weibull distribution for survival data, and the association between two models is induced by a latent Gaussian process. Parameters in the joint model are estimated under Bayesian framework and implemented using Markov chain Monte Carlo (MCMC) methods with WinBUGS software. The method is illustrated in the Ocular Hypertension Treatment Study to assess whether the variability of intraocular pressure is an independent risk of primary open-angle glaucoma. The performance of the method is also assessed by simulation studies.

El objetivo de este trabajo es evaluar la hipótesis causal de que el estatus socioeconómico de los estudiantes y la actitud de los estudiantes hacia la matemática son factores que determinan en gran medida los resultados académicos de los estudiantes costarricenses, medido a través del resultado en la prueba de alfabetización matemática de PISA 2012. Para esto, se define un modelo de medición de los constructos latentes y se estima el modelo estructural, tanto desde el enfoque clásico como desde el enfoque Bayesiano, para comparar ambos tipos de estimaciones. Los resultados muestran que las diferencias en la extracción socioeconómica de los estudiantes y en la actitud personal que estos tienen hacia el área matemática son un buen punto de partida para formular un modelo más extenso que contemple de mejor manera la complejidad de los factores sociales, institucionales y de contexto que inciden en el rendimiento académico de los jóvenes.

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.

We backtest 59 instruments and investigate the predictability of daily returns using
Bayesian variable selection methods. Through these models we show the importance
of variable selection and reduction of overtting. We also visualize how the driving
factors of daily returns from dierent classes vary over time. Predicting daily returns'
magnitude is again conrmed to be a hard task but we show that for some instruments
it is possible to achieve above average hit-rates that could lead to protable strategies.
Simulation results show that predictability levels of daily returns also vary over time.

In this paper, we discuss the analysis of complex health survey data by using multivariate modeling techniques. Our main interests are in design-based and model-based methods that aim at accounting for clustering, stratification and weighting effects. Our main interests are in clustering effects. Methods considered include generalized linear modeling with on pseudo-likelihood and generalized estimating equations, linear mixed models estimated by restricted maximum likelihood, and hierarchical Bayes techniques using Markov Chain Monte Carlo (MCMC) methods. The methods will be compared empirically, using data from a health interview and examination survey conducted in Finland in 2000 (Health-2000 Study). The data of the Health-2000 Study were collected using personal interviews, questionnaires and clinical examinations. A stratified two-stage cluster sampling design was used. The sampling design involved positive intra-cluster correlation for several study variables. We selected the s...

An effective approach for forecasting return volatility via threshold nonlinear heteroskedastic models of the daily asset price range is provided. The return is defined as the difference between the highest and lowest log intra-day asset price. A general model specification is proposed, allowing the intra-day high-low price range to depend nonlinearly on past information, or an exogenous variable such as US market information. The model captures aspects such as sign or size asymmetry and heteroskedasticity, which are commonly observed in financial markets. The focus is on parameter estimation, inference and volatility forecasting in a Bayesian framework. An MCMC sampling scheme is employed for estimation and shown to work well in simulation experiments. Finally, competing range-based and return-based heteroskedastic models are compared via out-of-sample forecast performance. Applied to six international financial market indices, the range-based threshold heteroskedastic models are well supported by the data in terms of finding significant threshold nonlinearity, diagnostic checking and volatility forecast performance under various volatility proxies.

Previous studies have drawn attention to substantial hydrological changes taking place in mountainous watersheds where hydrology is dominated by cryospheric processes. Modelling is an important tool for understanding these changes but is particularly challenging in mountainous terrain owing to scarcity of ground observations and uncertainty of model parameters across space and time. This study utilizes a Markov Chain Monte Carlo data assimilation approach to examine and evaluate the performance of a conceptual, degree-day snowmelt runoff model applied in the Tamor River basin in the eastern Nepalese Himalaya. The snowmelt runoff model is calibrated using daily streamflow from 2002 to 2006 with fairly high accuracy (average Nash-Sutcliffe metric~0.84, annual volume bias < 3%). The Markov Chain Monte Carlo approach constrains the parameters to which the model is most sensitive (e.g. lapse rate and recession coefficient) and maximizes model fit and performance. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall compared with simulations using observed station precipitation. The average snowmelt contribution to total runoff in the Tamor River basin for the 2002-2006 period is estimated to be 29.7 ± 2.9% (which includes 4.2 ± 0.9% from snowfall that promptly melts), whereas 70.3 ± 2.6% is attributed to contributions from rainfall. On average, the elevation zone in the 4000-5500 m range contributes the most to basin runoff, averaging 56.9 ± 3.6% of all snowmelt input and 28.9 ± 1.1% of all rainfall input to runoff. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall versus snowmelt compared with simulations using observed station precipitation. Model experiments indicate that the hydrograph itself does not constrain estimates of snowmelt versus rainfall contributions to total outflow but that this derives from the degree-day melting model. Lastly, we demonstrate that the data assimilation approach is useful for quantifying and reducing uncertainty related to model parameters and thus provides uncertainty bounds on snowmelt and rainfall contributions in such mountainous watersheds. Figure 4. Snow cover depletion curves from the Moderate Resolution Imaging Spectroradiometer 8-day maximum snow cover 500-m resolution product in the Tamor River basin from 2002 to 2006 in (a) zone 2, elevation range from 2500-4000 m, (b) zone 3, elevation range from 4000-5500 m and (c) zone 4, elevation range greater than 5500 m.

Homo naledi is a recently discovered species of fossil hominin from South Africa. A considerable amount is already known about H. naledi but some important questions remain unanswered. Here we report a study that addressed two of them: &quot; Where does H. naledi fit in the hominin evolutionary tree? &quot; and &quot; How old is it? &quot; We used a large supermatrix of craniodental characters for both early and late hominin species and Bayesian phylogenetic techniques to carry out three analyses. First, we performed a dated Bayesian analysis to generate estimates of the evolutionary relationships of fossil hominins including H. naledi. Then we employed Bayes factor tests to compare the strength of support for hypotheses about the relationships of H. naledi suggested by the best-estimate trees. Lastly, we carried out a resampling analysis to assess the accuracy of the age estimate for H. naledi yielded by the dated Bayesian analysis. The analyses strongly supported the hypothesis th...

Significant progress has been made towards the generalization of some well-known lifetime models, which have been successfully applied to problems arising in several areas of research. In this paper, some properties of the new Kumaraswamy exponential-Weibull (KwEW) distribution are provided. This distribution generalizes a number of well-known special lifetime models such as the Weibull, exponential, Rayleigh, modified Rayleigh, modified exponential and exponentiated Weibull distributions, among others. The beauty and importance of the new distribution lies in its ability to model monotone and nonmonotone failure rate functions, which are quite common in environmental studies. We derive some basic properties of the KwEW distribution including ordinary and incomplete moments, skewness, kurtosis, quantile and generating functions, mean deviations and Shannon entropy. The method of maximum likelihood and a Bayesian procedure are used for estimating the model parameters. By means of a real lifetime data set, we prove that the new distribution provides a better fit than the Kumaraswamy Weibull, Marshall-Olkin exponential-Weibull, extended Weibull, exponential-Weibull and Weibull models. The application indicates that the proposed model can give better fits than other well-known lifetime distributions.

Chapter written for the Handbook of Research Methods and Applications on Empirical Macroeconomics, edited by Nigar Hashimzade and Michael Thornton, forthcoming in 2012 (Edward Elgar Publishing). This chapter presents an introductory review of Bayesian methods for research in empirical macroeconomics

This work deals with the development of new theoretical and experimental techniques for the efficient estimation of thermophysical properties and source-term in micro and macro-scale. Two kinds of source term were studied: a constant and a time varying source term. The time wise variation of the source term had a sinusoidal and a
pulse form. Two devices were used for the sample heating: An electrical resistance and a laser diode. For the data acquisition, an infrared camera was used, providing a full cartography of properties of the medium and also non-contact temperature measurements. The direct problem was solved by the finite differences method, and two approaches were used for the solution of the inverse problem, depending on the time varying behavior of the source term. Both approaches deal with the parameters
estimation within the Bayesian framework, using the Markov Chain Monte Carlo (MCMC) method via the Metropolis Hastings (MH) algorithm for the constant source term, and the Kalman filter for the time-varying source term. The nodal strategy is presented as a method to deal with the large number of experimental data problems.
Experiments were carried out in a sample with well known thermophysical properties, determined by classical methods.

The phenomenon of sponsored search advertising - where advertisers pay a fee to Internet search engines to be displayed alongside organic (non-sponsored) web search results - is gaining ground as the largest source of revenues for search engines. Using a unique 6 month panel dataset of several hundred keywords collected from a large nationwide retailer that advertises on Google, we

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.