From statistics to knowledge

From inferential statistics to climate knowledge

Climate variability and change are risk factors for climate sensitive activities such as agriculture. Managing these risks requires “climate knowledge”, i.e. a sound understanding of causes and consequences of climate variability and knowledge of potential management options that are suitable in light of the climatic risks posed. Often such information about prognostic variables (e.g. yield, ra...

From Statistics to Beliefs

An intelligent agent uses known facts including statistical knowledge to assign degrees of belief to assertions it is un certain about We investigate three principled techniques for doing this All three are applications of the principle of indi erence because they assign equal degree of belief to all basic situations consistent with the knowledge base They di er because there are competing intu...

Extracting Prior Knowledge from Data Distribution to Migrate from Blind to Semi-Supervised Clustering

Although many studies have been conducted to improve the clustering efficiency, most of the state-of-art schemes suffer from the lack of robustness and stability. This paper is aimed at proposing an efficient approach to elicit prior knowledge in terms of must-link and cannot-link from the estimated distribution of raw data in order to convert a blind clustering problem into a semi-supervised o...

Analyzing Statistics with Background Knowledge from Linked Open Data

Background knowledge from Linked Open Data sources, such as DBpedia, Eurostat, and GADM, can be used to create both interpretations and advanced visualizations of statistical data. In this paper, we discuss methods of linking statistical data to Linked Open Data sources and the use of the Explain-a-LOD toolkit. The paper further shows exemplary findings and visualizations created by combining t...

Acquisition of 'Deep' Knowledge from Shallow Corpus Statistics