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Belief revision theory studies how an ideal doxastic agent should revise her beliefs when she receives new information. In part I I will first present the AGM theory of belief revision (Alchourrón & G...
Belief revision theory studies how an ideal doxastic agent should revise her beliefs when she receives new information. In part I, I have first presented the AGM theory of belief revision. Then I have...
Reasons for (prior) belief in Bayesian epistemology
Bayesian epistemology prior probabilities credences reasons for belief principle of insufficient reason belief revision belief formation
2016/6/12
Bayesian epistemology tells us how we should move from prior to posterior beliefs in light of new evidence or information, but says little about where our prior beliefs come from. It offers few resour...
Pragmatic Interests and Imprecise Belief
Belief Stakes Degrees of Belief Belief Strength Categorical Belief Stake-sensitive Stake-invariant Imprecise Belief Imprecise Probability Indeterminate Belief Sharpening
2016/5/27
Does the strength of a particular belief depend upon the significance we attach to it? Do we move from one context to another, remaining in the same doxastic state concerning p, yet holding a stronger...
Structured Learning for Taxonomy Induction with Belief Propagation
Structured Learning Taxonomy Induction Belief Propagation
2016/1/22
We present a structured learning approach to inducing hypernym taxonomies using a probabilistic graphical model formulation.Our model incorporates heterogeneous re-lational evidence about both hyperny...
In this paper we study the belief formation processes of a group of outside
observers making predictions about the actions of a player involved in a repeated
game. We document four main results. Fir...
From Uncertainty to Belief:Inferring the Specification Within
Uncertainty Belief Inferring the Specification Within
2015/8/7
Automatic tools for finding software errors require a set of specifications before they can check code: if they do not know what to check, they cannot find bugs. This paper presents a novel framework ...
“Bullshit”, as Harry Frankfurt writes in his recent book, On Bullshit, is a communication that pretends to be genuinely informative, but really is not. The person who talks bullshit, Frankfurt holds, ...
Decision Making Under Information Asymmetry:Experimental Evidence on Belief Refinements
Decision Choices and Conditions
2015/4/29
We explore how individuals make decisions in an operations management setting when there is information asymmetry between the firm and an outside investor. A common assumption in the signaling game li...
A Resource Belief-Curse:Oil and Individualism
Energy Price Policy Government and Politics Welfare or Wellbeing Energy Industry United States
2015/4/20
We study the correlation between a belief concerning individualism and a measure of luck in the US during the period 1983-2004. The measure of beliefs is the answer to a question related to whether th...
Scaling MCMC Inference and Belief Propagation to Large, Dense Graphical Models
machine learning graphical models
2014/12/18
With the physical constraints of semiconductor-based electronics becoming increasingly limiting in the past decade, single-core CPUs have given way to multi-core and distributed computing platforms. A...
Mispronunciation Detection via Dynamic Time Warping on Deep Belief Network-Based Posteriorgrams
mispronunciation detection dynamic time warping deep belief networks
2014/11/27
In this paper, we explore the use of deep belief network (DBN) posteriorgrams as input to our previously proposed comparison-based system for detecting word-level mispronunciation. The system works by...
Hopfield and Hebbian Models of Belief Polarization: Neural Networks, Social Contexts
Hopfield and Hebbian Models Belief Polarization Neural Networks Social Contexts
2014/8/8
In this paper, the authors develop two models of belief po-larization on signed social networks. First, we consider a Hop eld net-work model for opinion polarization. Although Hop eld networks were or...
Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units
Deep belief network restricted Boltzmann machine universal approxima-tion representational power Kullback-Leibler divergence,q-ary variable
2013/4/28
We generalize recent theoretical work on the minimal number of layers of narrow deep belief networks that can approximate any probability distribution on the states of their visible units arbitrarily ...
Belief Disagreements and Collateral Constraints
Belief disagreements heterogeneous priors collateral constraints leverage margin asymmetric disciplining short selling
2014/9/10
Belief disagreements have been suggested as a major contributing factor to the recent subprime mortgage crisis. This paper theoretically evaluates this hypothesis. I assume that optimists have limited...