Results 221 to 230 of about 5,031,588 (250)
Some of the next articles are maybe not open access.
Bayesianism: Objections and Rebuttals
2021Abstract While the laws of probability are rarely disputed, the question of how we should interpret probability judgments is less straightforward. Broadly, there are two ways to conceive of probability—either as an objective feature of the world, or as a subjective measure of our uncertainty. Both notions have their place in science, but
Norman Fenton, David Lagnado
openaire +1 more source
2020
In this chapter I analyse an objection to phenomenal conservatism to the effect that phenomenal conservatism is unacceptable because it is incompatible with Bayesianism. I consider a few responses and dismiss them as misled or problematic. Then, I argue that the objection doesn’t go through because it rests on an implausible formalization of the notion
openaire +1 more source
In this chapter I analyse an objection to phenomenal conservatism to the effect that phenomenal conservatism is unacceptable because it is incompatible with Bayesianism. I consider a few responses and dismiss them as misled or problematic. Then, I argue that the objection doesn’t go through because it rests on an implausible formalization of the notion
openaire +1 more source
BAYESIAN PROPAGATION FOR PERCEIVING MOVING OBJECTS
International Journal of Pattern Recognition and Artificial Intelligence, 2006In this paper we address the issue of how form and motion can be integrated in order to provide suitable information to attentively track multiple moving objects. Such integration is designed in a Bayesian framework, and a Belief Propagation technique is exploited to perform coherent form/motion labeling of regions of the observed scene.
Giuseppe Boccignone +4 more
openaire +1 more source
Objective Bayesianism with predicate languages
Synthese, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +3 more sources
On Bayesian Tracking of Extended Objects
2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2006In tracking and sensor data fusion targets of interest ewstare usually considered as point source objects; i.e. compared to the sensor resolution their extension is neglected. Due to the increasing resolution capabilities of modern sensors, however, this assumption is often no longer valid: Different scattering centers of an object can cause distinct ...
openaire +2 more sources
Objective Bayesian probabilistic logic
Journal of Algorithms, 2008This paper develops connections between objective Bayesian epistemology-which holds that the strengths of an agent's beliefs should be representable by probabilities, should be calibrated with evidence of empirical probability, and should otherwise be equivocal-and probabilistic logic.
openaire +2 more sources
Networks for Objective Bayesianism
2010In principle then we consider only a single model of the premisses, the probability function that has maximum entropy from all those that satisfy the premisses, and see whether that satisfies the conclusion. This model can be represented by a Bayesian network, and one can use this network to calculate the probability Y to attach to the conclusion ...
Rolf Haenni +3 more
openaire +1 more source
Bayesian object identification
Biometrika, 1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rue, H, Hurn, MA
openaire +2 more sources
Objective Bayesian Epistemology
2010We now turn to the final semantics for probabilistic logic that we consider in this book: the objective Bayesian semantics. In §7.1 we provide an introduction to objective Bayesian epistemology, which holds that the strengths of an agent’s beliefs should be representable by certain probability functions.
Rolf Haenni +3 more
openaire +1 more source
Journal of Applied Statistics, 1998
A Bayesian approach to object matching is presented. An object and a scene are each represented by features, such as critical points, line segments and surface patches, constrained by unary properties and contextual relations. The matching is presented as a labeling problem, where each feature in the scene is assigned (associated with) a feature of the
openaire +1 more source
A Bayesian approach to object matching is presented. An object and a scene are each represented by features, such as critical points, line segments and surface patches, constrained by unary properties and contextual relations. The matching is presented as a labeling problem, where each feature in the scene is assigned (associated with) a feature of the
openaire +1 more source