Now we are going to explain the various Graphical Models Applications in real life such as – Manufacturing, finance, Steel Production, Handwriting Recognition etc. At last, we will discuss the case study about the use of Graphical Models in the Volkswagen.

Let us now see few applications of graphical models:

Making the production of low cost and most reliable components at a high rate is possible. If all components of a production system (i.e. machine tool operation, dispatching etc) work on optimized parameters. Compute this by using graphical models.

Graphs, because they are pictures. They are particularly appropriate for presentation of financial information. Presenting financial information requires a careful understanding of both “what you want to say” and “who you need to say it to*“*.

To calculate the emission of carbon dioxide for Steel’s deoxidation we use Graph model.

We can use Graphical models to recognize hand writing. It can also use in several applications. It uses hand writing for identification.

We use Graphical models for diagnosing issues in it. It is used for help and to resolve them.

Graphical models provide a powerful framework for encoding. It provides the statistical structure of visual scenes. It also provides developing corresponding learning and inference algorithms.

Let us see the application of graphical model at Volkswagen:

a. Background

- The Volkswagen Corporation- It offers each customer the option of configuring their cars as individually as possible. A customer may choose from a wide variety of options which leads to high demand on logistics.

b. Problem

To attempt to achieve the ideal situation in which the customers provided by customized products built. By using components that arrive ‘‘just-in-time’’ for assembly, so that no stock is necessary at all. For this to be possible, it is necessary to plan the production process with high precision. This requires most accurate prediction of supply of the components.

c. Solution

- Volkswagen adopted the idea that given certain properties others may select independently of each other. Thus making decomposition of the joint probability distribution possible.
- Based on this decomposition, Volkswagen estimated the frequency of relevant combinations of properties. The system assumed that all cars in the sales database had these combinations.
- The decomposition allowed Volkswagen to remove the restriction to individual configurations. So, enlarge the samples that could check because of the decomposition.
- Finally, Volkswagen computed the supply of individual components from these estimates.
- To build the graphical model, Volkswagen created a relational network based on a catalog of technical and marketing rules. Volkswagen created it to describe constructible cars.

To create the relational model it needs 3 steps:

i) Translate the catalog of technical and marketing rules into a relational representation. Represent All rules that refer to the same set of attributes by a relation over this set. Starting from the Cartesian product of the attribute domains, discard all tuples that are incompatible with one of the rules.

ii) Organize these relations in a lattice structure defined by the subset relation on the domains of the relations. Use the maximal elements of the resulting lattice as the maximal cliques of the graph of a relational graphical model. Turn the relational model into an undirected graphical model by taking the graph structure of the relational model. Estimate and enhance it with probabilistic distribution functions, from a sales database.

iii) Turn the relational model into an undirected graphical model by taking the graph structure of the relational model. Estimate and enhance it with probabilistic distribution functions, from a sales database.

© 2020 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Statistics -- New Foundations, Toolbox, and Machine Learning Recipes
- Book: Classification and Regression In a Weekend - With Python
- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central