5,614 research outputs found

    Generalized Direct Sampling for Hierarchical Bayesian Models

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    We develop a new method to sample from posterior distributions in hierarchical models without using Markov chain Monte Carlo. This method, which is a variant of importance sampling ideas, is generally applicable to high-dimensional models involving large data sets. Samples are independent, so they can be collected in parallel, and we do not need to be concerned with issues like chain convergence and autocorrelation. Additionally, the method can be used to compute marginal likelihoods

    Scalable Inference of Customer Similarities from Interactions Data using Dirichlet Processes

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    Under the sociological theory of homophily, people who are similar to one another are more likely to interact with one another. Marketers often have access to data on interactions among customers from which, with homophily as a guiding principle, inferences could be made about the underlying similarities. However, larger networks face a quadratic explosion in the number of potential interactions that need to be modeled. This scalability problem renders probability models of social interactions computationally infeasible for all but the smallest networks. In this paper we develop a probabilistic framework for modeling customer interactions that is both grounded in the theory of homophily, and is flexible enough to account for random variation in who interacts with whom. In particular, we present a novel Bayesian nonparametric approach, using Dirichlet processes, to moderate the scalability problems that marketing researchers encounter when working with networked data. We find that this framework is a powerful way to draw insights into latent similarities of customers, and we discuss how marketers can apply these insights to segmentation and targeting activities

    Scalable Rejection Sampling for Bayesian Hierarchical Models

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    Bayesian hierarchical modeling is a popular approach to capturing unobserved heterogeneity across individual units. However, standard estimation methods such as Markov chain Monte Carlo (MCMC) can be impracticable for modeling outcomes from a large number of units. We develop a new method to sample from posterior distributions of Bayesian models, without using MCMC. Samples are independent, so they can be collected in parallel, and we do not need to be concerned with issues like chain convergence and autocorrelation. The algorithm is scalable under the weak assumption that individual units are conditionally independent, making it applicable for large datasets. It can also be used to compute marginal likelihoods

    The impact of social and temporal job demands and resources on emotional exhaustion and turnover intention among flight attendants

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    Based on a survey among flight attendants from a DACH-country-based airline, this study examines the effects and relative importance of social and temporal determinants of emotional exhaustion and turnover intention. Results suggest that scheduling satisfaction is the most influential predictor of both emotional exhaustion and turnover intention, followed by time pressure and surface acting for emotional Exhaustion and surface acting and organizational support for turnover intention. From a practical standpoint, these results thus suggest that the most important predictors of emotional exhaustion and turnover intention can be shaped and influenced quite well by management

    Against All Odds? National Sentiment and Wagering on European Football

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    This paper studies how national sentiment in the form of either a perception or a loyalty bias of bettors may affect pricing patterns on national wagering markets for international sport events. We show theoretically that both biases can be profitably exploited by bookmakers by way of price adjustment (odds shading). The former bias induces bookmakers to shade odds against the domestic team, the latter to adjust them in a way that depends on the demand elasticity of bettors for their national favorite. Analyzing empirically a unique data set of betting quotas from online bookmakers in twelve European countries for qualification games to the UEFA Euro 2008, we find evidence for systematic biases in the pricing of own national teams in the odds for win offered across countries. Variations in the sign and magnitude of these deviations can be explained by differences across countries in the respective strengths of the perception and loyalty biases among domestic bettors.Football, Home Bias, Investment, Patriotism
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