A toolbox of methods for probabilistic inference
نویسندگان
چکیده
We propose that probabilistic inference is supported by a mental toolbox that includes sampling and symmetry-based reasoning in addition to several other methods. To flesh out this claim we consider a spatial reasoning task and describe a number of different methods for solving the task. Several recent process-level accounts of probabilistic inference have focused on sampling, but we present an experiment that suggests that sampling alone does not adequately capture people’s inferences about our task.
منابع مشابه
Lifted Probabilistic Inference: A Guide for the Database Researcher
Modern knowledge bases such as Yago [14], DeepDive [19], and Google’s Knowledge Vault [6] are constructed from large corpora of text by using some form of supervised information extraction. The extracted data usually starts as a large probabilistic database, then its accuracy is improved by adding domain knowledge expressed as hard or soft constraints. Finally, the knowledge base can be queried...
متن کاملProbabilistic Argumentation for Decision Making A Toolbox and Applications
Argumentation frameworks developed in AI have greatly eased the developments of many kinds of intelligent systems. Recently, to deal with quantitative uncertainties, several authors integrate probabilities into such frameworks to propose probabilistic argumentation frameworks. However, the developments of intelligent systems using these new frameworks are still hindered by the lack of programmi...
متن کاملAn Introduction to Inference and Learning in Bayesian Networks
Bayesian networks (BNs) are modern tools for modeling phenomena in dynamic and static systems and are used in different subjects such as disease diagnosis, weather forecasting, decision making and clustering. A BN is a graphical-probabilistic model which represents causal relations among random variables and consists of a directed acyclic graph and a set of conditional probabilities. Structure...
متن کاملThyroid disorder diagnosis based on Mamdani fuzzy inference system classifier
Introduction: Classification and prediction are two most important applications of statistical methods in the field of medicine. According to this note that the classical classification are provided due to the clinical symptom and do not involve the use of specialized information and knowledge. Therefore, using a classifier that can combine all this information, is necessary. The aim of this s...
متن کاملPlanning by Probabilistic Inference
This paper presents and demonstrates a new approach to the problem of planning under uncertainty. Actions are treated as hidden variables, with their own prior distributions, in a probabilistic generative model involving actions and states. Planning is done by computing the posterior distribution over actions, conditioned on reaching the goal state within a specified number of steps. Under the ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2017