A booklet on information of graphical modes and its importance in organization

 

Title:   Prepare a booklet on information of graphical modes and its importance in organization

 

Description: 

 

·    What is graphical model in research?

Ø     A graphical model represents the probabilistic relationships among a set of variables. Nodes in the graph correspond to variables, and the absence of edges corresponds to conditional independence.

·    What is the importance of these graphical methods in presenting the data?

Ø     The aim of presenting scientific data graphically is to utilise the power of visual display to communicate information efficiently, while avoiding deception or confusion. This is important both in how we communicate our findings to others, and to our understanding and analysis of the data.

 

 

 

 

 

 

 

 

 

Outcomes  

 

Types of graphical models

Generally, probabilistic graphical models use a graph-based representation as the foundation for encoding a distribution over a multi-dimensional space and a graph that is a compact or factorized representation of a set of independences that hold in the specific distribution. Two branches of graphical representations of distributions are commonly used, namely, Bayesian networks and Markov random fields. Both families encompass the properties of factorization and independences, but they differ in the set of independences they can encode and the factorization of the distribution that they induce.

 

Undirected Graphical Model

P [ A , B , C , D ] = f A B [ A , B ] ⋅ f A C [ A , C ] ⋅ f A D [ A , D ] {\displaystyle P[A,B,C,D]=f_{AB}[A,B]\cdot f_{AC}[A,C]\cdot f_{AD}[A,D]} The undirected graph shown may have one of several interpretations; the common feature is that the presence of an edge implies some sort of dependence between the corresponding random variables. From this graph we might deduce that B , C , D {\displaystyle B,C,D} are all mutually independent, once A {\displaystyle A} is known, or (equivalently in this case) that

P [ A , B , C , D ] = f A B [ A , B ] ⋅ f A C [ A , C ] ⋅ f A D [ A , D ] {\displaystyle P[A,B,C,D]=f_{AB}[A,B]\cdot f_{AC}[A,C]\cdot f_{AD}[A,D]}

for some non-negative functions f A B , f A C , f A D {\displaystyle f_{AB},f_{AC},f_{AD}}