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

L\u2019eterogeneit\ue0 delle preferenze nel trasporto merci: un confronto tra diversi metodi per catturarla.

La struttura delle preferenze relative al trasporto merci \ue8 generalmente caratterizzata dalla presenza di una forte eterogeneit\ue0 dovuta alle specifiche caratteristiche della merce trasportata, dell\u2019azienda che commissiona il trasporto, degli operatori che se ne occupano, dell\u2019area in cui il trasporto si sviluppa. La classe dei modelli logit a parametri casuali si prefigge lo scopo di catturare tale eterogeneit\ue0 e di integrarla nei processi di stima. In questo articolo verranno confrontati diversi modelli all\u2019interno di questa classe. I modelli in esame differiscono per le assunzioni fatte circa la distribuzione dei parametri nella popolazione. In particolare, verranno considerati sia modelli basati su distribuzioni continue che modelli basati su distribuzioni discrete. I modelli saranno confrontati sulla base della loro capacit\ue0 di rappresentare l\u2019eterogeneit\ue0, della semplicit\ue0 di interpretazione dei risultati e delle difficolt\ue0 computazionali. Il confronto verr\ue0 effettuato sulla base di dati derivanti dalle preferenze dichiarate di 51 imprese marchigiane, appartenenti ai settori metallurgico (DJ) e mobile (DN), circa le caratteristiche del loro trasporto merci tipico

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.

Defining and Supporting Organizational Readiness in the Interactive Systems Framework for Dissemination and Implementaion

Introduction. In the implementation literature, organizational readiness is associated with an increased likelihood of achieving innovation outcomes. Organizational readiness consists of organizational capacity (general and innovation-specific) and organization motivation. Organizations who wish to get results from their innovations have an interest in making sure that certain factors and subcomponents are in place. However, having awareness that certain capacities and factors that influence motivation are linked to improved innovation outcomes does not necessarily help organizations to get “more ready.” There is a need for organizations to know if and how they can effectively put these factors and subcomponents into place. This dissertation set out to synthesize the strength of the evidence on how the Support System can use various techniques and interventions to build organizational readiness for implementing innovations, whether support system activities that specifically target readiness factors and subcomponents as part of an innovation implementation process demonstrate better innovation outcomes than non-targeted support system activities, and whether there were any circumstances under which readiness factors and subcomponents were less responsive to support system activities. Methods. A broad based research synthesis was used to gather information about what is known about providing support to enhance organizational readiness. To identify relevant articles, the search terms for each factor or subcomponent of readiness AND implementation AND each support strategy (tools OR training OR technical assistance OR quality assurance OR quality improvement) were entered into PsycInfo and PsychArticles (Behavioral Health), Medline and CINAHL (Health Care), and Science.gov and PAIS International databases (grey literature). 4397 articles were initially identified, with the full text of 297 articles were reviewed and coded following screening. 173 articles were retained and included in the syntheses. A coding form developed for this dissertation had an interrater reliability of κ = 0.76, with a percent agreement of 89.64. Results. The information gathered in this synthesis indicated that, 1) there is evidence that support system activities can enhance certain factors and subcomponents of organizational readiness, though the strength of evidence varied between factors and subcomponents, 2) support systems activities that target readiness are more likely to see changes in readiness outcomes than those that do not (log odds =1.13; SE = 0.46; p = 0.0137; OR = 3.1; 95% CI[1.23,7.48]), 3) support system activities that target readiness are more likely to achieve innovation outcomes than those that do not (log odds = 1.92; SE = 0.84; p = 0.0234; OR = 6.8; 95% CI [1.18,38.83]), and, 4) there are some statistical differences in articles that report changes in readiness versus those that do not. Conclusion. The findings indicate that there is evidence that organization readiness can be enhanced through the use of targeted support system activities. These findings have implications for service organizations that may be mandated or otherwise pressured to implement policies, program, or process by showing that there is potential to enhance the capabilities of organizations and therefore improve their ability to get positive innovation outcomes. Some next steps for research and practice are proposed

When we're backed into a corner, we learn how to fly: two ways local journalism can grow, thrive, & evolve to fit the needs of a new kind of local information ecosystem

Includes bibliographical references.2022 Fall.The local news industry and local information ecosystems face dual threats: collapsing business models that have taken with them traditional pipelines of community dialogue, and an often broken, divisive, still-top-down dialogue when conversation within our communities do happen. This dissertation proposes to address partial solutions for each concern in turn. First, by looking at how journalism teaching hospitals, long a steady source of news in the communities they call home, are formed and what makes them thrive. Then, in the interest of not recreating a broken system, by exploring the intersection of journalism and deliberative democracy, and proposing a method for local deliberative journalists to uncover the issues a community itself would most like to address

An Examination of the Overlapping Constraints for Maine Residents Participating in Outdoor Recreation Activities and Visiting Maine\u27s State Parks

Abstract The State of Maine is known to have a population of residents that are highly active in outdoor recreation activities and regularly visit Maine’s State Parks. Even though residents’ overall constraints to participating in outdoor recreation in Maine were found to be relatively low, a considerable portion of the population experiences significant barriers for participating in recreational activities while other analogous factors further constrain these residents from visiting Maine State Parks. For this study, a survey was conducted with a sample of Maine residents who were each asked to indicate the factors that limit their pursuit of outdoor recreational activities in general as well as specify, from a corresponding list of limitations, why they had not visited a Maine State Park before (n=399). The results of McNemar chi-square analyses found that being too busy, lack of knowledge, lack of interest, and family status were statistically significant overlapping constraints (p\u3c0.05)

A Markov Switching Re-evaluation of Event-Study Methodology

This paper reconsiders event-study methodology in light of evidences showing that Cumulative Abnormal Return (CAR) can result in misleading inferences about financial market efficiency and pre(post)-event behavior. In particular, CAR can be biased downward, due to the increased volatility on the event day and within the event window. We propose the use of Markov Switching Models to capture the effect of an event on security prices. We apply the proposed methodology to a set of 45 historical series on Credit Default Swap (CDS) quotes subject to multiple credit events, such as reviews for downgrading. Since CDSs provide insurance against the default of a particular company or sovereign entity, this study checks if market anticipates reviews for downgrading and evaluates the time period the announcements lag behind the market

Bayesian inference for marginal models under equality and inequality constraints

We develop a Bayesian framework for making inference on a class of marginal models for categorical variables, which is formulated through equality and/or inequality constraints on generalized logits, generalized log-odds ratios and similar higher-order interactions. A Markov chain Monte Carlo (MCMC) algorithm is used for parameters estimation and for computing the Bayes factor between competing models. The approach is illustrated through the application to a well-known dataset on social mobility

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