CIS 3453 - Bayesian Statistical Inference And Decision Making

Institution:

CUNY Bernard M Baruch College

Subject:

Computer Information Systems

Description:

3 hours; 3 credits A study of the techniques of Bayesian statistical inference and decision making. The course is designed to introduce the student to the general concepts of the Bayesian approach- utilization of all available information. Specific topics will include probability-objective and subjective; discrete and continuous models; prior and posterior analysis; decision theory; utility and decision making; value of sample information; and pre-posterior analysis. Differences and similarities between classical and Bayesian analysis are discussed. All areas of decision making will be applied to business problems. Prerequisites: STA 3154 and OPR 3450. Students interested in this course should see a department advisor.

Credits:

3.00

Credit Hours:

Prerequisites:

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

www.baruch.cuny.edu

Phone Number:

(646) 312-1000

Regional Accreditation:

Middle States Association of Colleges and Schools

Calendar System:

Semester

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