Predicate calculus.

The social sciences of quantificationfrom politics of large numbers to target-driven policies / by: (Electronic resources)

Modern quantification theoryjoint graphical display, biplots, and alternatives / by: (Electronic resources)

On spatio-temporal data modelling and uncertainty quantification using machine learning and information theory by: (Electronic resources)

Optimal quantification and symmetry by: (Electronic resources)

Uncertainty quantification using R by: (Electronic resources)

Measurement, mathematics and new quantification theory by: (Electronic resources)

Analysis of categorical data from historical perspectivesessays in honour of Shizuhiko Nishisato / by: (Electronic resources)

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)