The Structure and Dynamics of Scientific Theories: A Hierarchical Bayesian Perspective

Henderson, Leah; Goodman, Noah D.; Tenenbaum, Joshua B.; Woodward, James F.

doi:10.1086/651319

Cited by 52 publications

(45 citation statements)

References 18 publications

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“…These higher-level "choices that control choices" characterize the learner's "hypothesis space of hypothesis spaces"; they embody a more discrete, qualitative version of the hierarchical Bayesian ideas introduced in the previous section. They capture the role that intuitive theories or grammars play in providing frameworks for inductive inference in cognition, or the analogous role that higher-level frameworks or paradigms play in scientific theory building (Henderson, Goodman, Tenenbaum, & Woodward, 2010).…”

Section: Trading Off Parsimony and Goodness-of-fitmentioning

confidence: 99%

A tutorial introduction to Bayesian models of cognitive development

et al. 2011

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We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the what, the how, and the why of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for developmentalists. We emphasize a qualitative understanding of Bayesian inference, but also include information about additional resources for those interested in the cognitive science applications, mathematical foundations, or machine learning details in more depth. In addition, we discuss some important interpretation issues that often arise when evaluating Bayesian models in cognitive science.

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Section: Trading Off Parsimony and Goodness-of-fitmentioning

confidence: 99%

A tutorial introduction to Bayesian models of cognitive development

et al. 2011

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“…Rich, complex Bayesian models of epistemology, such as hierarchical models (Henderson et al 2010) or modified unification models (Myrvold 2003), provide a point of departure for constructing a formal model of consilience, which should help further clarify the role of consilience in science.…”

Section: Resultsmentioning

confidence: 99%

Historical Reconstruction: Gaining Epistemic Access to the Deep Past

Forber¹,

Griffith²

2011

Philosophy and Theory in Biology

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We discuss the scientific task of historical reconstruction and the problem of epistemic access. We argue that strong epistemic support for historical claims consists in the consilience of multiple independent lines of evidence, and analyze the impact hypothesis for the End-Cretaceous mass extinction to illustrate the accrual of epistemic support. Although there are elements of the impact hypothesis that enjoy strong epistemic support, the general conditions for this are strict, and help to clarify the difficulties associated with reconstructing the deep past.

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“…Formal epistemologists have largely neglected this phenomenon, perhaps because of the difficulty of formally representing it. In my own view, the best formal representation of this interconnectedness is found in the theory of Bayesian networks, developed by Judea Pearl (1988), and applied to epistemology by Bovens and Hartmann (2003) and Henderson et al (2010). These texts all contain further examples of empirical and scientific reasoning which is concerned with more than one level of explanation.…”

Section: Giving Up Consistency/coherencementioning

confidence: 99%

Inference to the Best Explanation Made Incoherent

Climenhaga¹

2017

Journal of Philosophy

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Defenders of Inference to the Best Explanation (IBE) claim that explanatory factors should play an important role in empirical inference. They disagree, however, about how exactly to formulate this role. In particular, they disagree about whether to formulate IBE as an inference rule for full beliefs or for degrees of belief, and, if it is formulated as a rule for degrees of belief, how this rule relates to Bayesianism. In this essay I advance a new argument against non-Bayesian versions of IBE that arises when we are concerned with multiple levels of explanation of some phenomenon. I show that in many such cases, following IBE as an inference rule for full belief leads to deductively inconsistent beliefs, and following IBE as a non-Bayesian updating rule for degrees of belief leads to (synchronically) probabilistically incoherent degrees of belief.Proponents of Inference to the Best Explanation (IBE) claim that our inferences should give explanatory considerations a central role. Beyond this general agreement, however, they have differed on precisely how explanation should inform inference. A particular area of controversy has been the relation of IBE to Bayesianism. Should IBE be formulated in terms of full beliefs, as in traditional epistemology, or in terms of degrees of belief, as in Bayesian epistemology? If it is formulated in the latter way, is it compatible with Bayesian epistemology?In this essay I advance a new argument against non-Bayesian formulations of IBE. This includes both traditional formulations of IBE in terms of full belief and non-Bayesian formulations of IBE in terms of degrees of belief. I show that in some instances, IBE for full belief licenses deductively inconsistent inferences from the same evidence. In similar instances, following non-Bayesian IBE updating rules for degrees of belief leads to probabilistically incoherent credences.

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The Structure and Dynamics of Scientific Theories: A Hierarchical Bayesian Perspective

Cited by 52 publications

References 18 publications

A tutorial introduction to Bayesian models of cognitive development

A tutorial introduction to Bayesian models of cognitive development

Historical Reconstruction: Gaining Epistemic Access to the Deep Past

Inference to the Best Explanation Made Incoherent

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