Inference for the physical sciences

Inference for the physical sciences.

There is a disconnect between developments in modern data analysis and some parts of the physical sciences in which they could find ready use. This introduction, and this issue, provides resources to help experimental researchers access modern data analysis tools and exposure for analysts to extant challenges in physical science. We include a table of resources connecting statistical and physic...

Visualization for the Physical Sciences

Close collaboration with other scientific fields is seen as an important goal for the visualization community by leading researchers in visualization. Yet, engaging in a scientific collaboration can be challenging. Physical sciences, with its array of research directions, provide many exciting challenges for a visualization scientist which in turn create ample possibilities for collaboration. W...

Physical Chemistry for the Biological Sciences

ing read in many parts of the book. Lastly, the topic is extremely broad and could have focused on smaller segments of PI3K signaling or on one effector, etc. As a common interest book, the book lacks focus and direction, but it does contain a large wealth of data and information that might be useful as a review or reference to the researcher interested in PI3K signaling or cell cycle regulatio...

Bayesian Logical DataAnalysis for the Physical Sciences

Ecological Inference in the Social Sciences.

Ecological inference is a problem of partial identification, and therefore reliable precise conclusions are rarely possible without the collection of individual level (identifying) data. Without such data, sensitivity analyses provide the only recourse. In this paper we review and critique approaches to ecological inference in the social sciences, and describe in detail hierarchical models, whi...