How to model mutually exclusive events based on independent causal pathways in Bayesian network models

This is supported by ERC project ERC-2013-AdG339182-BAYES_KNOWLEDGE

Keterlibatan Konsumen Dalam Kebaruan Produk

Involvement is a key word in the context of product novelty, when businesses are required to become more competitive in product innovation. This point of view forms the basis for understanding the two concepts of business existence. First, the demand to develop new products is directed to product innovation because technology is increasingly developing. Novelty needs to keep pace with technological advances as well as be market-oriented. Second, the business builds a collaborative commitment with its customers. This indicates a position where the relationship with the market demands an equal role. Businesses no longer take distance in building relationships with their markets. Thus consumers will gothrough a dynamic process in the experience of using the product, and businesses will continue to consistently motivate consumer learning in the process of product novelty adoption. This study aims to explain consumer’s learning experience on product novelty from the process of consumer involvement in the holistic experience of responding to product novelty. A combined quantitative-qualitative approach (mixed-method approach) is applied. The quantitative approach was carried out using SEM analysis with the SmartPLS tool on 113 respondents, while the qualitative approach was carried out using the Interpretative Phenomenological Analysis (IPA) technique on participant narratives. Furthermore, from the two approaches, an integration process was carried out to find the grand theme of consumer learning experiences in the context of their involvement in responding to product novelty. Through this research, it can be seen that the product novelty learning experience is formed through initial involvement to recognize, andcontinued involvement in using the product. The product novelty learning experience is the involvement of consumers in realizing essential realities, practical realities, and contextual realities.&nbsp

Learning Bayesian Networks That Enable Full Propagation of Evidence

This paper builds on recent developments in Bayesian network (BN) structure learning under the controversial assumption that the input variables are dependent. This assumption can be viewed as a learning constraint geared towards cases where the input variables are known or assumed to be dependent. It addresses the problem of learning multiple disjoint subgraphs that do not enable full propagation of evidence. This problem is highly prevalent in cases where the sample size of the input data is low with respect to the dimensionality of the model, which is often the case when working with real data. The paper presents a novel hybrid structure learning algorithm, called SaiyanH, that addresses this issue. The results show that this constraint helps the algorithm to estimate the number of true edges with higher accuracy compared to the state-of-the-art. Out of the 13 algorithms investigated, the results rank SaiyanH 4th in reconstructing the true DAG, with accuracy scores lower by 8.1% (F1), 10.2% (BSF), and 19.5% (SHD) compared to the top ranked algorithm, and higher by 75.5% (F1), 118% (BSF), and 4.3% (SHD) compared to the bottom ranked algorithm. Overall, the results suggest that the proposed algorithm discovers satisfactorily accurate connected DAGs in cases where other algorithms produce multiple disjoint subgraphs that often underfit the true graph

Analyzing the Simonshaven Case using Bayesian Networks

This paper is one in a series of analyses of the Dutch Simonshaven murder case, each using a different modeling approach. We adopted a Bayesian network (BN)–based approach which requires us to determine the relevant hypotheses and evidence in the case and their relationships (captured as a directed acyclic graph) along with explicit prior conditional probabilities. This means that both the graph structure and probabilities had to be defined using subjective judgments about the causal, and other, connections between variables and the strength and nature of the evidence. Determining if a useful BN could be quickly constructed by a small group using the previously established idioms‐based approach which provides a generic method for translating legal cases into BNs, was a key aim. The model described was built by the authors during a workshop dedicated to the case at the Isaac Newton Institute, Cambridge, in September 2016. The total effort involved was approximately 26 h (i.e., an average of 6 h per author). With the basic assumptions described in the paper, the posterior probability of guilt once all the evidence is entered is 74%. The paper describes a formal evaluation of the model, using sensitivity analysis, to determine how robust the model conclusions are to key subjective prior probabilities over a full range of what may be deemed “reasonable” from both defense and prosecution perspectives. The results show that the model is reasonably robust—pointing not only generally to a reasonably high posterior probability of guilt but also generally below the 95% threshold expected in criminal law. Given the constraints on building a complex model so quickly, there are inevitably weaknesses; hence, the paper describes these and how they might be addressed, including how to take account of supplementary case information not known at the time of the workshop

Modelling competing legal arguments using Bayesian model comparison and averaging

Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and ‘average’ Bayesian models of legal arguments that have been built independently and with no attempt to make them consistent in terms of variables, causal assumptions or parameterization. The approach involves assessing whether competing models of legal arguments are explained or predict facts uncovered before or during the trial process. Those models that are more heavily disconfirmed by the facts are given lower weight, as model plausibility measures, in the Bayesian model comparison and averaging framework adopted. In this way a plurality of arguments is allowed yet a single judgement based on all arguments is possible and rational

Crime reconstruction and the role of trace materials from crime scene to court

Crime reconstruction takes place in a complex ecosystem and needs to be responsive to the context of each case. For accurate, reproducible and transparent crime reconstructions to take place, a holistic approach is needed that considers the different stakeholders, different types of trace material, integral human decision‐making and interconnected nature of the forensic science process. For robust reconstruction, there needs to be a consideration of both the distinctive types of trace material that can contribute to the reconstruction, and an understanding of the interplay of human decision‐making within reconstruction approaches. In addition, it is also necessary to consider source attribution of a trace material in addition to the activities that led to the generation, identification, transfer, and persistence of the trace. This requires explicit and tacit forms of knowledge, and an incorporation of the inherent uncertainty and risk in the reconstruction approach. The communication of conclusions reached in a crime reconstruction that address what the evidence means is also an important consideration given the different requirements of intelligence and evidence. Therefore, undertaking a crime reconstruction within a holistic framework that seeks to incorporate the complexity of the forensic science ecosystem is valuable for achieving a problem solving approach that offers reproducible, transparent reconstructions with a clear articulation of risk and uncertainty that can be of value to investigators and the courts. This article is categorized under: Forensic Science in Action/Crime Scene Investigation > Crime Scene Reconstruction Forensic Science in Action/Crime Scene Investigation > From Traces to Intelligence and Evidenc

Constructing the world: Active causal learning in cognition

Humans are adept at constructing causal models of the world that can support prediction, explanation, simulation-based reasoning, planning and control. In this thesis I explore how people learn about the causal world interacting with it, and how they represent and modify their causal knowledge as they gather evidence. Over 10 experiments and modelling, I show that interventional and temporal cues, along with top-down hierarchical constraints, inform the gradual evolution and adaptation of increasingly rich causal representations. Chapters 1 and 2 develop a rational analysis of the problems of learning and representing causal structure, and choosing interventions, that perturb the world in ways that reveal its structure. Chapters 3--5 focus on structure learning over sequences of discrete trials, in which learners can intervene by setting variables within a causal system and observe the consequences. The second half of the thesis generalises beyond the discrete trial learning case, exploring interventional causal learning in situations where events occur in continuous time (Chapters 6 and 7); and in spatiotemporally rich physical "microworlds" (Chapter 8). Throughout the experiments, I find that both children and adults are robust active causal learners, able to deal with noise and complexity even as normative judgment and intervention selection become radically intractable. To explain their success, I develop scalable process level accounts of both causal structure learning and intervention selection inspired by approximation algorithms in machine learning. I show that my models can better explain patterns of behaviour than a range of alternatives as well as shedding light on the source of common biases including confirmatory testing, anchoring effects and probability matching. Finally, I propose a close relationship between active learning and active aspects of cognition including thinking, decision making and executive control