We present a model of inductive inference that includes, as special cases, Bayesian reasoning, case-based reasoning, and rule-based reasoning. This unified framework allows us to examine how the various modes of inductive inference can be combined and how their relative weights change endogenously. For example, we establish conditions under which an agent who does not know the structure of the data generating process will decrease, over the course of her reasoning, the weight of credence put on Bayesian vs. non-Bayesian reasoning. We illustrate circumstances under which probabilistic models are used until an unexpected outcome occurs, whereupon the agent resorts to more basic reasoning tech-niques, such as case-based and rule-based reasonin...
Inductive probabilistic reasoning is understood as the application of inference patterns that use st...
Inductive inference allows humans to make powerful generalizations from sparse data when learning ab...
This chapter1 concerns the relation between statistics and inductive logic. I start by describing in...
We present a model of inductive inference that includes, as special cases, Bayesian reasoning, case-...
International audienceWe present a model of inductive inference that includes, as special cases, Bay...
Inductive inference allows humans to make powerful generalizations from sparse data when learning ab...
We argue that human inductive generalization is best explained in a Bayesian framework, rather than ...
Many of the central problems of cognitive science are problems of induction, calling for uncertain i...
This chapter1 concerns the relation between statistics and inductive logic. I start by describing in...
This chapter1 concerns inductive logic in relation to statistics. I start by introducing a general n...
Everyday inductive reasoning draws on many kinds of knowledge, including knowledge about relationshi...
Text materials such as those introduced by McKoon and Ratcliff (1986) have been repeatedly used to s...
A central problem in artificial intelligence is reasoning under uncertainty. This thesis views induc...
Inductive Inference is reasoning that justifies change from one state of full belief or absolute cer...
A framework for inductive inference in logic is presented: a Model Inference Problem is defined, and...
Inductive probabilistic reasoning is understood as the application of inference patterns that use st...
Inductive inference allows humans to make powerful generalizations from sparse data when learning ab...
This chapter1 concerns the relation between statistics and inductive logic. I start by describing in...
We present a model of inductive inference that includes, as special cases, Bayesian reasoning, case-...
International audienceWe present a model of inductive inference that includes, as special cases, Bay...
Inductive inference allows humans to make powerful generalizations from sparse data when learning ab...
We argue that human inductive generalization is best explained in a Bayesian framework, rather than ...
Many of the central problems of cognitive science are problems of induction, calling for uncertain i...
This chapter1 concerns the relation between statistics and inductive logic. I start by describing in...
This chapter1 concerns inductive logic in relation to statistics. I start by introducing a general n...
Everyday inductive reasoning draws on many kinds of knowledge, including knowledge about relationshi...
Text materials such as those introduced by McKoon and Ratcliff (1986) have been repeatedly used to s...
A central problem in artificial intelligence is reasoning under uncertainty. This thesis views induc...
Inductive Inference is reasoning that justifies change from one state of full belief or absolute cer...
A framework for inductive inference in logic is presented: a Model Inference Problem is defined, and...
Inductive probabilistic reasoning is understood as the application of inference patterns that use st...
Inductive inference allows humans to make powerful generalizations from sparse data when learning ab...
This chapter1 concerns the relation between statistics and inductive logic. I start by describing in...