The Bayesian Theory of Confirmation, Idealizations and Approximations in Science

1998 ◽  
pp. 281-289
Author(s):  
Erdinç Sayan
Keyword(s):  
Bayesian Model ◽  
Background Knowledge ◽  
Bayesian Theory ◽  
Confirmation Theory ◽  
Actual Observation ◽  
Logical Entailment ◽  
Old Evidence

My focus in this paper is on how the basic Bayesian model can be amended to reflect the role of idealizations and approximations in the confirmation or disconfirmation of any hypothesis. I suggest the following as a plausible way of incorporating idealizations and approximations into the Bayesian condition for incremental confirmation: Theory T is confirmed by observation P relative to background knowledge B iff Pr(PΔ│T&(T&I ├ PT)&B) > Pr(PΔ│~T&(T&I├PT)andB), where I is the conjunction of idealizations and approximations used in deriving the prediction PT from T, P􀀧 expresses the discrepancy between the prediction PT and the actual observation P, and ├ stands for logical entailment. This formulation has the virtue of explicitly taking into account the essential use made of idealizations and approximations as well as the fact that theoretically based predictions that utilize such assumptions will not, in general, exactly fit the data. A non-probabilistic analogue of the confirmation condition above that I offer avoids the 'old evidence problem,' which has been a headache for classical Bayesianism.

scholarly journals Being a Patient Representative Is More Work Than Being a Patient Advocate—Done Right, It Is Also Likely to Be More Effective

2021 ◽  
Vol 28 (2) ◽  
pp. 1017-1019
Author(s):  
Richard Wassersug
Keyword(s):  
Health Outcomes ◽  
Background Knowledge ◽  
Positive Influence ◽  
Patient Advocate ◽  
Different Types ◽  
Health Related ◽  
Patient Representative

For a patient to be effective as a “patient representative” within a health-related organization, work and more than just accepting an honorific title is required. I argue that for a patient to be most effective as a patient representative requires different types of background knowledge and commitment than being a “patient advocate”. Patients need to be cautious about how, when, and where they take on an official role of either an “advocate” or “representative”, if they truly want to be a positive influence on health outcomes.

scholarly journals Predictors of Successful Reading Comprehension in Bilingual Adults: The Role of Reading Strategies and Language Proficiency

Languages ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 18
Author(s):  
Deanna C. Friesen ◽  
Bailey Frid
Keyword(s):  
Reading Comprehension ◽  
Language Proficiency ◽  
Text Analysis ◽  
Reading Strategies ◽  
Background Knowledge ◽  
Strategy Use ◽  
Dominant Language ◽  
Analysis Strategies

The current study investigated the type of strategies that English–French bilingual adults utilize when reading in their dominant and non-dominant languages and which of these strategies are associated with reading comprehension success. Thirty-nine participants read short texts while reporting aloud what they were thinking as they read. Following each passage, readers answered three comprehension questions. Questions either required information found directly in the text (literal question) or required a necessary inference or an elaborative inference. Readers reported more necessary and elaborative inferences and referred to more background knowledge in their dominant language than in their non-dominant language. Engaging in both text analysis strategies and meaning extraction strategies predicted reading comprehension success in both languages, with differences observed depending on the type of question posed. Results are discussed with respect to how strategy use supports the development of text representations.

Beyond response output: More logical than we think

2009 ◽  
Vol 32 (1) ◽  
pp. 87-88 ◽  
Author(s):  
Wim De Neys
Keyword(s):  
Bayesian Model ◽  
Response Selection ◽  
Data Fitting ◽  
Output Data ◽  
The Core ◽  
Response Output ◽  
Exclusive Focus ◽  
Better Than

AbstractOaksford & Chater (O&C) rely on a data fitting approach to show that a Bayesian model captures the core reasoning data better than its logicist rivals. The problem is that O&C's modeling has focused exclusively on response output data. I argue that this exclusive focus is biasing their conclusions. Recent studies that focused on the processes that resulted in the response selection are more positive for the role of logic.

scholarly journals A Bayesian Account of Memory for Text

2019 ◽  
Author(s):  
Mark Andrews
Keyword(s):  
Bayesian Inference ◽  
Strong Evidence ◽  
Bayesian Model ◽  
Background Knowledge ◽  
Predictive Inference ◽  
Memory Representation ◽  
British English ◽  
Text Memory ◽  
Memory Experiment ◽  
Memory For Text

The study of memory for texts has had an long tradition of research in psychology. According to most general accounts, the recognition or recall of items in a text is based on querying a memory representation that is built up on the basis of background knowledge. The objective of this paper is to describe and thoroughly test a Bayesian model of these general accounts. In particular, we present a model that describes how we use our background knowledge to form memories in terms of Bayesian inference of statistical patterns in the text, followed by posterior predictive inference of the words that are typical of those inferred patterns. This provides us with precise predictions about which words will be remembered, whether veridically or erroneously, from any given text. We tested these predictions using behavioural data from a memory experiment using a large sample of randomly chosen texts from a representative corpus of British English. The results show that the probability of remembering any given word in the text, whether falsely or veridically, is well predicted by the Bayesian model. Moreover, compared to nontrivial alternative models of text memory, by every measure used in the analyses, the predictions of the Bayesian model were superior, often overwhelmingly so. We conclude that these results provide strong evidence in favour of the Bayesian account of text memory that we have presented in this paper.

Introduction to Remanufacturing and Reverse Logistics

2013 ◽  
pp. 1-17
Keyword(s):  
Reverse Logistics ◽  
Background Knowledge ◽  
Environment Protection

This chapter introduces the background knowledge of the main targeted problem considered in this book (i.e., remanufacturing and its associated reverse logistics). The chapter starts with an introduction about the role of remanufacturing in environment protection. Then, the related studies dealing with the remanufacturing are outlined in the background section, which is followed by a discussion about the work dedicated to the reverse logistics. Finally, the conclusion drawn in the last section closes this chapter.

Gene Expression Mining Guided by Background Knowledge

2011 ◽  
pp. 268-292
Author(s):  
Jirí Kléma ◽  
Filip Železný ◽  
Igor Trajkovski ◽  
Filip Karel ◽  
Bruno Crémilleux
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Descriptive Analysis ◽  
Background Knowledge ◽  
Heterogeneous Data ◽  
Expression Data ◽  
Heterogeneous Data Sources ◽  
Biological Entities ◽  
Quantitative Association Rule

This chapter points out the role of genomic background knowledge in gene expression data mining. The authors demonstrate its application in several tasks such as relational descriptive analysis, constraintbased knowledge discovery, feature selection and construction or quantitative association rule mining. The chapter also accentuates diversity of background knowledge. In genomics, it can be stored in formats such as free texts, ontologies, pathways, links among biological entities, and many others. The authors hope that understanding of automated integration of heterogeneous data sources helps researchers to reach compact and transparent as well as biologically valid and plausible results of their gene-expression data analysis.

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