Aptech Systems, Inc. Worldwide Headquarters
Aptech Systems, Inc.
2350 East Germann Road, Suite #21
Chandler, AZ 85286
Ready to Get Started?
Request Quote & Product Information
Training & Events
Step-by-step, informative lessons for those who want to dive into GAUSS and achieve their goals, fast.
Have a Specific Question?
Q&A: Register and Login
Premier Support and Platinum Premier Support are annually renewable membership programs that provide you with important benefits including technical support, product maintenance, and substantial cost-saving features for your GAUSS System or the GAUSS Engine.
Join our community to see why our users are considered some of the most active and helpful in the industry!
Where to Buy
Recent Tagsapplications character vectors CML CMLMT Constrained Optimization datasets dates dlibrary dllcall error error handling errors Excel FANPACMT file i/o floating network GAUSS Engine graphics GUI hotkeys installation Java API license licensing linux loading data matrices matrix matrix manipulation Maxlik MaxLikMT Memory optimization Optmum output PQG graphics procs RAM random numbers string functions strings structures threading Time Series writing data
Time Series 2.0 MT
Find out more now
Time Series MT 2.1
COMT vs. OPMT vs. MLMT vs. CMLMT
Tags: asked February 18, 2014
What are the differences between COMT vs. OPMT vs. MLMT vs. CMLMT?
OPMT optimizes an objective function allowing for bounds on parameters, but no other constraints on parameters.
COMT optimizes an objective function subject to general constraints on parameters, linear or nonlinear, equality or inequality, as well as bounds.
MLMT is like OPMT except that it knows about datasets and optimizes a log-likelihood objective function, that is, it produces maximum likelihood estimates. It also provides for a variety of types of statistical inference.
CMLMT (also known as CMMT) is like COMT except that it knows about datasets and optimizes a log-likelihood objective function, that is, it produces maximum likelihood estimates. It also provides for a variety of types of statistical inference.