Eliciting prior information to enhance the predictive performance of bayesian graphical models

Both knowledge-based systems and statistical models are typically concerned with making predictions about future observables. Here we focus on assessment of predictive performance and provide two techniques for improving the predictive performance of Bayesian graphical models. First, we present Bayesian model averaging, a technique for accounting for model uncertainty.

Second, we describe a technique for eliciting a prior distribution for competing models from domain experts. We explore the predictive performance of both techniques in the context of a urological diagnostic problem.     

Bayesian Analysis of the Prototypal Search Model

Bayesian analysis for a simple but widely applied dynamic programming model is obtained. The setting is the prototypal job-search model. The general case of wage and duration data, with potential censoring, is studied. The optimality condition implied by the dynamic programming setup is fully imposed. The posterior distribution reveals a “ridge” reflecting the characteristic nonstandard nature of the inference problem. Marginal distributions and moments are obtained in a canonical parameterization after a suitable approximation. The adequacy of the approximation is easily assessed. Simulation is applied to study alternative parameterizations and prior robustness and to facilitate prior elicitations. Finally, we illustrate the applicability of our methods by giving posterior distributions for the elasticities of unemployment durations and reemployment wages with respect to unemployment income. Our analysis is easy to implement and all computations are simple to perform.     

Single-parameter inference based on partial prior information

Partial specification of a prior distribution can be appealing to an analyst, but there is no conventional way to update a partial prior. In this paper, we show how a framework for Bayesian updating with data can be based on the Dirichlet(a) process. Within this framework, partial information predictors generalize standard minimax predictors and have interesting multiple-point shrinkage properties. Approximations to partial-information estimators for squared error loss are defined straightforwardly, and an estimate of the mean shrinks the sample mean. The proposed updating of the partial prior is a consequence of four natural requirements when the Dirichlet parameter a is continuous. Namely, the updated partial posterior should be calculable from knowledge of only the data and partial prior, it should be faithful to the full posterior distribution, it should assign positive probability to every observed event {X,}, and it should not assign probability to unobserved events not included in the partial prior specification.     

课堂教学要注重的几个关键点

教师是课堂教学的首要要素。课堂教学的基本要求是不但要使学生知道所讲的内容,而且要了解其精髓、了解其源头和关联。教学内容的选择要做到“少而精”。运用启发式教学,通过课堂讨论进一步调动学生的学习主动性。     

Explosion Probability of Unexploded Ordnance: Expert Beliefs

This article reports on a study to quantify expert beliefs about the explosion probability of unexploded ordnance (UXO). Some 1,976 sites at closed military bases in the United States are contaminated with UXO and are slated for cleanup, at an estimated cost of $15–140 billion. Because no available technology can guarantee 100% removal of UXO, information about explosion probability is needed to assess the residual risks of civilian reuse of closed military bases and to make decisions about how much to invest in cleanup. This study elicited probability distributions for the chance of UXO explosion from 25 experts in explosive ordnance disposal, all of whom have had field experience in UXO identification and deactivation. The study considered six different scenarios: three different types of UXO handled in two different ways (one involving children and the other involving construction workers). We also asked the experts to rank by sensitivity to explosion 20 different kinds of UXO found at a case study site at Fort Ord, California. We found that the experts do not agree about the probability of UXO explosion, with significant differences among experts in their mean estimates of explosion probabilities and in the amount of uncertainty that they express in their estimates. In three of the six scenarios, the divergence was so great that the average of all the expert probability distributions was statistically indistinguishable from a uniform (0, 1) distribution—suggesting that the sum of expert opinion provides no information at all about the explosion risk. The experts' opinions on the relative sensitivity to explosion of the 20 UXO items also diverged. The average correlation between rankings of any pair of experts was 0.41, which, statistically, is barely significant (p= 0.049) at the 95% confidence level. Thus, one expert's rankings provide little predictive information about another's rankings. The lack of consensus among experts suggests that empirical studies are needed to better understand the explosion risks of UXO.     

A Mechanism for Eliciting Probabilities

This paper describes a direct revelation mechanism for eliciting agents' subjective probabilities. The game induced by the mechanism has a dominant strategy equilibrium in which the players reveal their subjective probabilities.     

Time-discrete beta-process model for interval-censored survival data

Grouped survival data with possible interval censoring arise in a variety of settings. This paper presents nonparametric Bayes methods for the analysis of such data. The random cumulative hazard, common to every subject, is assumed to be a realization of a Lévy process. A time-discrete beta process, introduced by Hjort, is considered for modeling the prior process. A sampling-based Monte Carlo algorithm is used to find posterior estimates of several quantities of interest. The methodology presented here is used to check further modeling assumptions. Also, the methodology developed in this paper is illustrated with data for the times to cosmetic deterioration of breast-cancer patients. An extension of the methodology is presented to deal with two interval-censored times in tandem data (as with some AIDS incubation data).     

Judged Lethality: How Much People Seem to Know Depends Upon How They Are Asked

Four formally equivalent response modes were used to elicit laypeople's beliefs regarding the lethality of various potential causes of death. Results showed that respondents had an articulated core of beliefs about lethality that yielded similar orderings of maladies by lethality regardless of the response mode used. Moreover, this subjective ordering was fairly similar to that revealed by public health statistics. However, the absolute estimates of lethality produced by the different response modes varied enormously. Depending upon the mode used, respondents were seen to greatly overestimate or greatly underestimate lethality. The implications of these discrepancies for public education and risk analysis are explored.     

Classification Algorithm for Skin Color (CASCo): A new tool to measure skin color in social science research

Objective

A growing body of literature reveals that skin color has significant effects on people's income, health, education, and employment. However, the ways in which skin color has been measured in empirical research have been criticized for being inaccurate, if not subjective and biased.

Objective

Introduce an objective, automatic, accessible and customizable Classification Algorithm for Skin Color (CASCo).

Methods

We review the methods traditionally used to measure skin color (verbal scales, visual aids or color palettes, photo elicitation, spectrometers and image-based algorithms), noting their shortcomings. We highlight the need for a different tool to measure skin color

Results

We present CASCo, a (social researcher-friendly) Python library that uses face detection, skin segmentation and k-means clustering algorithms to determine the skin tone category of portraits.

Conclusion

After assessing the merits and shortcomings of all the methods available, we argue CASCo is well equipped to overcome most challenges and objections posed against its alternatives. While acknowledging its limitations, we contend that CASCo should complement researchers. toolkit in this area.