Operating Research
Quantitative analysis
Quantitative analyst in finance, someone who applies mathematics, among others stochastic calculus, to finance.
Quantitative analysis (chemistry), in analytical chemistry, the measurements of quantities of substances produced in reactions rather than simply noting the nature of the reactions
Quantitative analysis may also be:
Statistics and statistical analysis techniques rather than the use of mathematical material in social science.
Decision theory model
Decision theory in mathematics and statistics is concerned with identifying the values, uncertainties and other issues relevant in a given decision and the resulting optimal decision. It is sometimes called game theory
Normative and descriptive decision theory
Most of decision theory is normative or prescriptive, i.e., it is concerned with identifying the best decision to take, assuming an ideal decision maker who is fully informed, able to compute with perfect accuracy, and fully rational. The practical application of this prescriptive approach (how people actually make decisions) is called decision analysis, and aimed at finding tools, methodologies and software to help people make better decisions. The most systematic and comprehensive software tools developed in this way are called decision support systems.
Decision Trees
A decision tree (or tree diagram) is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal. Another use of decision trees is as a descriptive means for calculating conditional probabilities.
Linear Programming
In mathematics, linear programming (LP) is a technique for optimization of a linear objective function, subject to linear equality and linear inequality constraints. Informally, linear programming determines the way to achieve the best outcome (such as maximum profit or lowest cost) in a given mathematical model and given some list of requirements represented as linear equations.
More formally, given a polytope (for example, a polygon or a polyhedron), and a real-valued affine function
defined on this polytope, a linear programming method will find a point in the polytope where this function has the smallest (or largest) value. Such points may not exist, but if they do, searching through the polytope vertices is guaranteed to find at least one of them.