Predicate calculus.

The social sciences of quantificationfrom politics of large numbers to target-driven policies / by: (書目-電子資源)

Modern quantification theoryjoint graphical display, biplots, and alternatives / by: (書目-電子資源)

On spatio-temporal data modelling and uncertainty quantification using machine learning and information theory by: (書目-電子資源)

Optimal quantification and symmetry by: (書目-電子資源)

Uncertainty quantification using R by: (書目-電子資源)

Measurement, mathematics and new quantification theory by: (書目-電子資源)

Analysis of categorical data from historical perspectivesessays in honour of Shizuhiko Nishisato / by: (書目-電子資源)

Philosophy.

Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.

Statistics for Social Sciences, Humanities, Law.

Symmetry (Mathematics)

Data Analysis and Big Data.

Uncertainty

Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

Psychometrics.

Statistical Theory and Methods.

Uncertainty (Information theory)

Earth and Environmental Sciences.

Renewable Energy.

Statistics and Computing.

Applied Statistics.

R (Computer program language)

Applied Probability.

Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.

Predicate calculus.

Statistics for Business, Management, Economics, Finance, Insurance.

Computer Modelling.

Discrete Optimization.

Methodology of the Social Sciences.

Machine learning.

Social sciences

Statistics

Philosophy of the Social Sciences.

Operations Research and Decision Theory.

Categories (Mathematics)