Bayesian inference for Hidden Markov Model

� Hidden Markov Models can be considered an extension of mixture models, allowing for dependent observations. In a hierarchical Bayesian framework, we show how Reversible Jump Markov Chain Monte Carlo techniques can be used to estimate the parameters of a model, as well as the number of regimes. We consider a mixture of normal distributions characterized by different means and variances under each regime, extending the model proposed by Robert et al. (2000), based on a mixture of zero mean normal distributions.

Bayesian inference through encompassing priors and importance sampling for a class of marginal models for categorical data

We develop a Bayesian approach for selecting the model which is the most supported by the data within a class of marginal models for categorical variables formulated through equality and/or inequality constraints on generalised logits (local, global, continuation or reverse continuation), generalised log-odds ratios and similar higher-order interactions. For each constrained model, the prior distribution of the model parameters is formulated following the encompassing prior approach. Then, model selection is performed by using Bayes factors which are estimated by an importance sampling method. The approach is illustrated through three applications involving some datasets, which also include explanatory variables. In connection with one of these examples, a sensitivity analysis to the prior specification is also considered

Bayesian inference for Latent Class model via MCMC with application to capture-recapture data

In this paper we propose a Bayesian Latent Class model for capture-recapture data. Through two appliations, the first concerning a sample of snowshoe hares and the second concerning a sample of diabetics in a small Italian town, we show how the proposed approach may be effectively used to obtain point estimates and credibility intervals for the size of a closed-population. To estimate the model we use the Reversible Jump algorithm and the Delayed Rejection strategy to improve its efficiency.Bayesian Inference; Capture-recapture; Delayed Rejection; Latent Class model; Reversible Jump.

Mode choice models with attribute cutoffs analysis: the case of freight transport in the Marche region

This paper shows that, when modelling freight demand, taking into consideration the presence of attribute cutoffs is important and has relevant repercussions on the estimates of service attributes coefficients. In this paper we focus on mode choice models for freight transport demand in the Marche region in Italy. Specific reference is paid to furniture and metallurgic productive sectors given their relevance for the region and their potential vocation for intermodal transport. Preference elicitation is done using choice based conjoint analysis. The study shows that there is a structural difference among the two sectors and that they have heterogeneous preferences

Bayesian hidden Markov models for financial data

Hidden Markov Models can be considered as an extension of mixture models, which allows for dependent observations and makes them suitable for financial applications. In a hierarchical Bayesian framework, we show how reversible jump Markov chain Monte Carlo techniques can be used to estimate the parameters of the model, as well as the number of regimes. An application to exchange rate dynamics modeling is presented

A spatial regression analysis of Colombia’s narcodeforestation with factor decomposition of multiple predictors

In the current accelerated process of global warming, forest conservation is becoming more difficult to address in developing countries, where woodlands are often fueling the illegal economy. In Colombia, the issue of narcodeforestation is of great concern, because of the ramification of narcoactivities that are affecting forests, such as agribusinesses and cattle ranching for money laundering. In this study, we use spatially explicit regressions incorporating a factor decomposition of predictors through principal component analysis to understand the impact of coca plantations on global and local-scale deforestation in Colombia. At national level we find a positive and statistically significant relationship between coca crops and deforestation. At the regional level, in two out of four regions, it appears that coca is causing deforestation, especially in the Department of Northern Santander and on the Pacific coast. The spatial models used reveal not only a direct effect but also positive and significant spillover effects, in line with the conjecture that narcodeforestation is not only due to the quest for new areas to expand coca-cultivation, which would determine a loss of forest only in the municipality where coca cultivation increases, but also to the need to launder illegal profits, or create clandestine routes and airplane strips, which can affect forests also in nearby municipalities

Willingness to pay confidence interval estimation methods. Comparisons and extensions

This paper systematically compares methods to build confidence intervals for willingness to pay measures in a discrete choice context. It contributes to the literature by including methods developed in other research fields. Monte Carlo simulations are used to assess the performance of all the methods considered. The various scenarios evaluated reveal a certain skewness in the estimated willingness to pay distribution. This should be reflected in the confidence intervals. Results show that the commonly used Delta method, producing symmetric intervals around the point estimate, often fails to account for such a skewness. Both the Fieller method and the likelihood ratio test inversion method produce more realistic confidence intervals. Some bootstrap methods also perform reasonably well. Finally, empirical data are used to illustrate an application of the methods considere

A Bayesian analysis of neutron spin echo data on polymer coated gold nanoparticles in aqueous solutions

We present a neutron spin echo study (NSE) of the nanosecond dynamics of polyethylene glycol (PEG) functionalised nanosized gold particles dissolved in D$_2$O at two temperatures and two different PEG molecular weights. The analysis of the NSE data was performed by applying a Bayesian approach to the description of time correlation function decays in terms of exponential terms, recently proved to be theoretically rigorous. This approach, which addresses in a direct way the fundamental issue of model choice in any dynamical analysis, provides here a guide to the most statistically supported way to follow the decay of the Intermediate Scattering Functions I(Q, t) by basing on statistical grounds the choice of the number of terms required for the description of the nanosecond dynamics of the studied systems. Then, the presented analysis avoids from the start resorting to a pre-selected framework and can be considered as model free. By comparing the results of PEG coated nanoparticles with those obtained in PEG2000 solutions, we were able to disentangle the translational diffusion of the nanoparticles from the internal dynamics of the polymer grafted to them, and to show that the polymer corona relaxation follows a pure exponential decay in agreement with the behavior predicted by coarse grained molecular dynamics simulations and theoretical models. This methodology has one further advantage: in the presence of a complex dynamical scenario I(Q,t) is often described in terms of the Kohlrausch-Williams-Watts function that can implicitly represent a distribution of relaxation times. By choosing to describe the I(Q,t) as a sum of exponential functions and with the support of the Bayesian approach, we can explicitly determine when a finer-structure analysis of the dynamical complexity of the system exists according to the available data without the risk of overparametrisation

Scuba diving tourism and the challenge of sustainability: evidence from an explorative study in North African-Mediterranean countries

Purpose Scuba diving tourism is reputed to be a potential low-impact recreational activity that allow environmental conservation and socioeconomic benefits for local communities. Few studies have addressed the issue of sustainability of scuba diving tourism through the simultaneously investigation on the economic and socio-cultural aspects and its implications for tourism development. This study aims to examine the scuba diving tourism in three under-explored North African tourism destinations with high ecotourist potential. The authors present an exploratory picture of scuba diving tourist demand, divers' preferences, motivations for recreational diving experiences and their propensity towards conservation. Design/methodology/approach The authors developed a case study research strategy collecting profile data on 123 divers. Furthermore, regression analysis was performed to investigate the divers' preferences, motivations and propensity towards conservation. Findings The divers' limited number, the presence of mainly local seasonal tourists and a moderate propensity towards conservation influence the potential of the diving tourism segment to generate significant socioeconomic benefits for local sustainable development in these destinations. However, establishing a marine protected area (MPA) could foster the development of a long-term strategy for scuba diving tourism, improve conservation awareness and increase divers' satisfaction. Practical implications Diverse profiles, preferences and motivations can provide tools to sustainably manage and preserve coastal and marine biodiversity, while also maximising the quality of the recreational experience. One of the most effective site-based strategies to orient the diving sector towards sustainability involves the design and strengthening of MPAs. Originality/value The research provides an original contribution to the debate on sustainable tourism strategies by demonstrating how the study of economic and socio-cultural aspects of scuba diving could provide guidelines to orient the tourism development of marine and coastal areas towards the principles of sustainability (also through the establishment of MPAs). The findings present an overview of the sustainability of the scuba diving tourism segment by investigating the preferences, motivations and inclination towards conservation among tourists for whom the diving experience is not a core holiday activity

Bayesian Approach for X-Ray and Neutron Scattering Spectroscopy

The rapidly improving performance of inelastic scattering instruments has prompted tremendous advances in our knowledge of the high-frequency dynamics of disordered systems, yet also imposing new demands to the data analysis and interpretation. This ongoing effort is likely to reach soon an impasse, unless new protocols are developed in the data modeling. This need stems from the increasingly detailed information sought for in typical line shape measurements, which often touches or crosses the boundaries imposed by the limited experimental accuracy. Given this scenario, the risk of a bias and an over-parametrized data modeling represents a concrete threat for further advances in the field. Being aware of the severity of the problem, we illustrate here the new hopes brought in this area by Bayesian inference methods. Making reference to recent literature results, we demonstrate the superior ability of these methods in providing a probabilistic and evidence-based modeling of experimental data. Most importantly, this approach can enable hypothesis test involving competitive line shape models and is intrinsically equipped with natural antidotes against the risk of over-parametrization as it naturally enforces the Occam maximum parsimony principle, which favors intrinsically simple models over overly complex ones