Inferential statistics to verify prediction models

Inferential ecosystem models, from network data to prediction.

Recent developments suggest that predictive modeling could begin to play a larger role not only for data analysis, but also for data collection. We address the example of efficient wireless sensor networks, where inferential ecosystem models can be used to weigh the value of an observation against the cost of data collection. Transmission costs make observations "expensive"; networks will typic...

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...

A ( brief ) Introduction to Inferential Statistics

The eld of statistics can be divided into two branches, descriptive statistics and inferential statistics. Descriptive statistics, as its name suggests, is concerned with describing a population, or a sample from that population, in terms of numerical measures that are computed from data. Such a numerical measure is called a statistic. If the data used in the computation comes from the whole po...

Effect Size vs Inferential Statistics

Inferential Models

Probability is a useful tool for describing uncertainty, so it is natural to strive for a system of statistical inference based on probabilities for or against various hypotheses. But existing probabilistic inference methods struggle to provide a meaningful interpretation of the probabilities across experiments in sufficient generality. In this paper we further develop a promising new approach ...