Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks

Maximum ' entropy ' framework ' for ' inference ' of ' cell ' population ' heterogeneity ' in ' signaling ' networks '

On Maximum Entropy and Inference

Maximum entropy is a powerful concept that entails a sharp separation between relevant and irrelevant variables. It is typically invoked in inference, once an assumption is made on what the relevant variables are, in order to estimate a model from data, that affords predictions on all other (dependent) variables. Conversely, maximum entropy can be invoked to retrieve the relevant variables (suf...

Population heterogeneity and causal inference.

Population heterogeneity is ubiquitous in social science. The very objective of social science research is not to discover abstract and universal laws but to understand population heterogeneity. Due to population heterogeneity, causal inference with observational data in social science is impossible without strong assumptions. Researchers have long been concerned with two potential sources of b...

Maximum Entropy, Time Series and Statistical Inference

A brief discussion is given of the traditional version of the Maximum Entropy Method, including a review of some of the criticism that has been made in regard to its use in statistical inference. Motivated by these questions, a modified version of the method is then proposed and applied to an example in order to demonstrate its use with a given time series.

a framework for identifying and prioritizing factors affecting customers’ online shopping behavior in iran

the purpose of this study is identifying effective factors which make customers shop online in iran and investigating the importance of discovered factors in online customers’ decision. in the identifying phase, to discover the factors affecting online shopping behavior of customers in iran, the derived reference model summarizing antecedents of online shopping proposed by change et al. was us...