Interactive Instruction in Bayesian Inference

An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction. These principles concern coherence, personalization, signaling, segmenting, multimedia, spatial contiguity, and pretraining. Principles of self-explanation and interactivity are also applied. Four experiments on the Mammography Problem showed that these principles help participants answer the questions at significantly improved rates. Nonetheless, in novel interactivity conditions, performance was lowered suggesting that more interaction can add more difficulty for participants. Overall, a leap forward in accuracy was found, with more than twice the participant accuracy of previous work. This indicates that an instructional approach to improving human performance in Bayesian inference is a promising direction.     

An evolutionary Bayesian belief network methodology for optimum management of groundwater contamination

An integrated methodology, based on Bayesian belief network (BBN) and evolutionary multi-objective optimization (EMO), is proposed for combining available evidence to help water managers evaluate implications, including costs and benefits of alternative actions, and suggest best decision pathways under uncertainty. A Bayesian belief network is a probabilistic graphical model that represents a set of variables and their probabilistic relationships, which also captures historical information about these dependencies. In complex applications where the task of defining the network could be difficult, the proposed methodology can be used in validation of the network structure and the parameters of the probabilistic relationship. Furthermore, in decision problems where it is difficult to choose appropriate combinations of interventions, the states of key variables under the full range of management options cannot be analyzed using a Bayesian belief network alone as a decision support tool. The proposed optimization method is used to deal with complexity in learning about actions and probabilities and also to perform inference. The optimization algorithm generates the state variable values which are fed into the Bayesian belief network. It is possible then to calculate the probabilities for all nodes in the network (belief propagation). Once the probabilities of all the linked nodes have been updated, the objective function values are returned to the optimization tool and the process is repeated. The proposed integrated methodology can help in dealing with uncertainties in decision making pertaining to human behavior. It also eliminates the shortcoming of Bayesian belief networks in introducing boundary constraints on probability of state values of the variables. The effectiveness of the proposed methodology is examined in optimum management of groundwater contamination risks for a well field capture zone outside Copenhagen city.     

概率粗糙集三支决策在线快速计算算法研究

随着大数据和物联网技术的不断发展,动态在线计算已经成为了一种常见的计算模式,在动态在线计算中进行不确定问题的推理和求解是一项具有挑战性的新议题。概率粗糙集三支决策理论是一种处理不确定性知识挖掘的有效工具,根据在线计算模式中数据同步增减的动态特点,提出了一种概率粗糙集三支决策的在线计算方法。首先,以内存滑动窗口模式对在线动态计算的数据变化特点进行理论建模;然后,根据上述模型中在线计算的数据变化模式,推导出不同类型数据变化模式下的三支决策条件概率及三支区域的变化规律;最后,提出了一种新型在线快速计算算法,其获取的三支决策规则与经典概率三支决策算法是等效的。通过与经典三支决策计算算法的多组对比实验,验证了提出的在线快速计算算法的高效性与稳定性。     

Uncovering strengths and weaknesses of radial visualizations--an empirical approach

Radial visualizations play an important role in the information visualization community. But the decision to choose a radial coordinate system is rather based on intuition than on scientific foundations. The empirical approach presented in this paper aims at uncovering strengths and weaknesses of radial visualizations by comparing them to equivalent ones in Cartesian coordinate systems. We identified memorizing positions of visual elements as a generic task when working with visualizations. A first study with 674 participants provides a broad data spectrum for exploring differences between the two visualization types. A second, complementing study with fewer participants focuses on further questions raised by the first study. Our findings document that Cartesian visualizations tend to outperform their radial counterparts especially with respect to answer times. Nonetheless, radial visualization seem to be more appropriate for focusing on a particular data dimension.     

Axiomatisation of fully probabilistic design

This text provides background of fully probabilistic design (FPD) of decision-making strategies and shows that it is a proper extension of the standard Bayesian decision making. FPD essentially minimises Kullback–Leibler divergence of closed-loop model on its ideal counterpart. The inspection of the background is important as the current motivation for FPD is mostly heuristic one, while the technical development of FPD confirms its far reaching possibilities. FPD unifies and simplifies subtasks and elements of decision making under uncertainty. For instance, (i) both system model and decision preferences are expressed in common probabilistic language; (ii) optimisation is simplified due to existence of explicit minimiser in stochastic dynamic programming; (iii) DM methodology for single and multiple aims is unified; (iv) a way is open to completion and sharing non-probabilistic and probabilistic knowledge and preferences met in knowledge and preference elicitation as well as unsupervised cooperation of decision makers.     

Evaluating the Impact of User Characteristics and Different Layouts on an Interactive Visualization for Decision Making

There is increasing evidence that user characteristics can have a significant impact on visualization effectiveness, suggesting that visualizations could be designed to better fit each user's specific needs. Most studies to date, however, have looked at static visualizations. Studies considering interactive visualizations have only looked at a limited number of user characteristics, and consider either low‐level tasks (e.g., value retrieval), or high‐level tasks (in particular: discovery), but not both. This paper contributes to this line of work by looking at the impact of a large set of user characteristics on user performance with interactive visualizations, for both low and high‐level tasks. We focus on interactive visualizations that support decision making, exemplified by a visualization known as Value Charts. We include in the study two versions of ValueCharts that differ in terms of layout, to ascertain whether layout mediates the impact of individual differences and could be considered as a form of personalization. Our key findings are that (i) performance with low and high‐level tasks is affected by different user characteristics, and (ii) users with low visual working memory perform better with a horizontal layout. We discuss how these findings can inform the provision of personalized support to visualization processing.     

Bayesian methods for control loop monitoring and diagnosis

There exist many algorithms for control performance monitoring. There are also many algorithms available for process monitoring. There are, however, few methods available for synthesis of various monitoring techniques to form a diagnosing system for optimal decision making. This paper is concerned with establishing and demonstrating a novel probabilistic diagnostic framework for control loop monitoring. The new framework possesses a number of desired properties, including, for example, quantitative probabilistic diagnosis, flexibility in synthesizing different monitoring techniques, robustness in the presence of missing data or missing variables, ease of expanding or shrinking the diagnosing system, ability to incorporate a priori process knowledge, and capability for decision making. As the backbone of the proposed framework, the emerging Bayesian methods are introduced and shown to be the appropriate tools for solving the problem of concern. Several representative control loop diagnostic problems are formulated under the Bayesian framework and their solutions are demonstrated through examples. The experiences and challenges learned from industrial applications of Bayesian methods are summarized and some of future research directions are discussed.     

贝叶斯决策网在智能决策系统中的应用研究

面向复杂的决策问题，提出基于贝叶斯决策网在智能决策支持系统中的应用，通过决策网可以直接计算不同行动方案下的效用，直接为决策者服务。文章深入阐述了贝叶斯决策网的语义、语法、效用函数的构造和详细探讨了在求解复杂决策问题中决策模型构建和推理算法实现的方法和途径。最后建立了供应链管理中零售商预定冰激凌的实例，实际运行表明把基于贝叶斯决策网的智能决策系统用于复杂决策问题是有效的，尤其适用于多角色参与的决策问题。     

分布式环境下连续概率Skyline查询

Skyline计算是多准则决策,数据挖掘和数据库可视化的重要操作。移动对象在运动过程中,由于位置信息的不确定,导致局部各数据点间的支配关系不稳定,从而影响全局概率Skyline集合。针对分布式环境下不确定移动对象的连续概率Skyline查询更新进行研究,提出了一种降低通信开销的连续概率Skyline查询的有效算法CDPS-UMO,该算法在局部节点中对局部概率Skyline点的变化进行跟踪;提出了有效的排序方法和反馈机制,大大降低了通信开销和计算代价;提出一种基本算法naive,与CDPS-UMO进行了对比实验,实验结果证明了算法的有效性。     

Comparing Node‐Link and Node‐Link‐Group Visualizations From An Enjoyment Perspective

While evaluation studies in visualization often involve traditional performance measurements, there has been a concerted effort to move beyond time and accuracy. Of these alternative aspects, memorability and recall of visualizations have been recently considered, but other aspects such as enjoyment and engagement are not as well explored. We study the enjoyment of two different visualization methods through a user study. In particular, we describe the results of a three‐phase experiment comparing the enjoyment of two different visualizations of the same relational data: node‐link and node‐link‐group visualizations. The results indicate that the participants in this study found node‐link‐group visualizations more enjoyable than node‐link visualizations.     

A Survey on Visual Analysis Approaches for Financial Data

Market participants and businesses have made tremendous efforts to make the best decisions in a timely manner under varying economic and business circumstances. As such, decision‐making processes based on Financial data have been a popular topic in industries. However, analyzing Financial data is a non‐trivial task due to large volume, diversity and complexity, and this has led to rapid research and development of visualizations and visual analytics systems for Financial data exploration. Often, the development of such systems requires researchers to collaborate with Financial domain experts to better extract requirements and challenges in their tasks. Work to systematically study and gather the task requirements and to acquire an overview of existing visualizations and visual analytics systems that have been applied in Financial domains with respect to real‐world data sets has not been completed. To this end, we perform a comprehensive survey of visualizations and visual analytics. In this work, we categorize Financial systems in terms of data sources, applied automated techniques, visualization techniques, interaction, and evaluation methods. For the categorization and characterization, we utilize existing taxonomies of visualization and interaction. In addition, we present task requirements extracted from interviews with domain experts in order to help researchers design better systems with detailed goals.     

对弧进行删除的Bayes网络近似方法

弧的删除是一种对Bayes网络模型进行近似的方法。文中以Kullback-Leibler偏差作为近似网络和原网络概率分布误差的测度,给出了近似网络在此测度意义下的最优参数。同时,也给出了通过对原网络删除多条弧进行近似的启发式算法,当给定一个误差上界时,可以使用此算法寻找满足误差要求的近似网络。     

Preference solutions of probability decision making with rim quantifiers

This article extends the quantifier‐guided aggregation method to include probabilistic information. A general framework for the preference solution of decision making under an uncertainty problem is proposed, which can include decision making under ignorance and decision making under risk methods as special cases with some specific preference parameters. Almost all the properties, especially the monotonicity property, are kept in this general form. With the generating function representation of the Regular Increasing Monotone (RIM) quantifier, some properties of the RIM quantifier are discussed. A parameterized RIM quantifier to represent the valuation preference for probabilistic decision making is proposed. Then the risk attitude representation method is integrated in this quantifier‐guided probabilistic decision making model to make it a general form of decision making under uncertainty. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1253–1271, 2005.     

Constructing,evaluating and visualizing value and utility functions for decision support

Formal methods of decision analysis can help to structure a decision making process and to communicate reasons for decisions transparently. Objectives hierarchies and associated value and utility functions are useful instruments for supporting such decision making processes by structuring and quantifying the preferences of decision makers or stakeholders. Common multi-attribute decision analysis software products support such decision making processes but they can often not represent complex preference structures and visualize uncertainty induced by uncertain predictions of the consequences of decision alternatives. To stimulate strengthening these aspects in decision support processes, we propose a set of visualization tools and provide a software package for constructing, evaluating and visualizing value and utility functions. In these tools we emphasize flexibility in value aggregation schemes and consideration and communication of prediction uncertainty. The use of these tools is demonstrated with an illustrative example of river management decision support.     

A simulation based multi-attribute group decision making technique with decision constraints

The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a useful technique for solving Multi Attribute Group Decision Making (MAGDM) problems. In MAGDM, the performance scores of the alternatives and the weights of assessment attributes are mostly vague. Therefore, using of deterministic data throughout decision making process may lead to inaccurate results. In order to overcome inherent vagueness and uncertainty, various fuzzy MAGDM techniques were presented in the literature. However, these fuzzy MAGDM techniques are focused on expected and extreme values, which are sometimes insufficient for the precise determination of alternatives’ preference structure. In this paper, in order to eliminate the limitations of deterministic and fuzzy MAGDM methods, we present a probabilistic methodology, which is based on TOPSIS and Monte-Carlo simulation of triangular data. In addition to its straightforward application and thanks to its versatility, simulation enables decision makers to incorporate some decision constraints into decision-making process. Two illustrative examples are also given to show the effectiveness of the proposed methodology. The method is also compared with a fuzzy TOPSIS technique from the literature.     

层次信息可视化技术研究综述

随着掌控的层次信息在数量和规模上的急剧增长,用户迫切需要高效的可视化工具在理解信息和制定决策过程中对认知行为进行辅助.首先对层次结构进行介绍,给出层次可视化的设计目标;然后讨论了层次可视化技术的分类问题,并尝试提出一种较为合理且便于阐述的表现-维度分类框架;随后基于上述分类框架,重点分析现有主要层次可视化技术的基本原理和特征;最后比较总结各类技术,并对层次可视化可能的研究方向及面临的挑战做出展望.     

Continuous improvement through knowledge-guided analysis in experience feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector.     

How information visualization novices construct visualizations

It remains challenging for information visualization novices to rapidly construct visualizations during exploratory data analysis. We conducted an exploratory laboratory study in which information visualization novices explored fictitious sales data by communicating visualization specifications to a human mediator, who rapidly constructed the visualizations using commercial visualization software. We found that three activities were central to the iterative visualization construction process: data attribute selection, visual template selection, and visual mapping specification. The major barriers faced by the participants were translating questions into data attributes, designing visual mappings, and interpreting the visualizations. Partial specification was common, and the participants used simple heuristics and preferred visualizations they were already familiar with, such as bar, line and pie charts. We derived abstract models from our observations that describe barriers in the data exploration process and uncovered how information visualization novices think about visualization specifications. Our findings support the need for tools that suggest potential visualizations and support iterative refinement, that provide explanations and help with learning, and that are tightly integrated into tool support for the overall visual analytics process.     

Decision making on the sole basis of statistical likelihood

This paper presents a new axiomatic decision theory for choice under uncertainty. Unlike Bayesian decision theory where uncertainty is represented by a probability function, in our theory, uncertainty is given in the form of a likelihood function extracted from statistical evidence. The likelihood principle in statistics stipulates that likelihood functions encode all relevant information obtainable from experimental data. In particular, we do not assume any knowledge of prior probabilities. Consequently, a Bayesian conversion of likelihoods to posterior probabilities is not possible in our setting. We make an assumption that defines the likelihood of a set of hypotheses as the maximum likelihood over the elements of the set. We justify an axiomatic system similar to that used by von Neumann and Morgenstern for choice under risk. Our main result is a representation theorem using the new concept of binary utility. We also discuss how ambiguity attitudes are handled. Applied to the statistical inference problem, our theory suggests a novel solution. The results in this paper could be useful for probabilistic model selection.     

Flexible Linked Axes for multivariate data visualization

Multivariate data visualization is a classic topic, for which many solutions have been proposed, each with its own strengths and weaknesses. In standard solutions the structure of the visualization is fixed, we explore how to give the user more freedom to define visualizations. Our new approach is based on the usage of Flexible Linked Axes: The user is enabled to define a visualization by drawing and linking axes on a canvas. Each axis has an associated attribute and range, which can be adapted. Links between pairs of axes are used to show data in either scatter plot- or Parallel Coordinates Plot-style. Flexible Linked Axes enable users to define a wide variety of different visualizations. These include standard methods, such as scatter plot matrices, radar charts, and PCPs [11]; less well known approaches, such as Hyperboxes [1], TimeWheels [17], and many-to-many relational parallel coordinate displays [14]; and also custom visualizations, consisting of combinations of scatter plots and PCPs. Furthermore, our method allows users to define composite visualizations that automatically support brushing and linking. We have discussed our approach with ten prospective users, who found the concept easy to understand and highly promising.