MESS Analytics

In probability theory the Cox Process aka the doubly stochastic Poisson process is a point process which is a generalisation of a Poisson process that the intensity vaires across a underliying mathematical space (e.g space/time) is itself a stochastic process.

The intention of using graphs is to represent the complex data abstractly, determining strength and direction of relationship between objects. Probabilistic Graphical Models (PGM) are mathematical structures bring together graph and probability theory as a powerful flexible framework to model complex large collections of random variables with complex interactions. There are many fields and numerous applications that through construction of PGM have supported making predictions and decision making under uncertainty.

*Coming Soon*

Having completed a cloud computing pilot project (over two years) and having project expirence in development and deployment on the AWS, I am very keen to learn in a more structured approach and fill missing knowledge gaps. I have completed the Developer associate exam and now attempting the SysOps Administrator in preparation for the DevOps professional.

Following from part one on introduction into real analysis. We will continue by exploring further real sequences, series and functions.

Some interesting real analysis proofs.

Part One we looked at selecting of regressors through a number of criteria and the process that may be adopted. Part Two will focus on Residual Analysis, Diagnostic Check and Multicollinearity Model Adequacy: Residual Analysis In determining a linear model the usual assumption in the specification are not always guaranteed. For least squares the estimation of parameters as well as prediction…