What Is a Bayesian Network?

Nowadays, most of the functions are handled using various forms of statistical data and analytical graphs. These methods of surveying are common in many professional sectors, be it keeping a track of temperature for every month in a year or noting down the monthly revenues of a company. In a way, these different analytical methods have made the surveys more accurate and much easier. As a result, people who are not even experts in the field of analytical science and statistics easily understand different probability charts, demographics and other types of statistical tables. However, the way to solve different types of probability related complex problems differ from type to type; the most frequently used model is the Bayesian network. It is one of the most preferred networks and hence a Bayesian network has proven to be quite useful in analysing various data.

What is the Bayesian network?

Statistical studies are done based on different types of models and graphical analytical methods. Bayesian network is also a model used for studying different statistical information based on conditional probabilities. The model consists of nodes and edges, forming a tree that is important for establishing a conditional connection between all the nodes. The nodes represent different variables, probabilistic quantities, random variables and other types of parameters. The edges represent the conditional dependencies of one variable over another. These dependencies are depicted by arrowheads and the interdependency is directed in the direction of the arrow. However, if some of the nodes are not connected, then that means they are independent of each other and hence no conditions prevail.

Components of the Bayesian network model

A Bayesian network model is made up of different components, all of which are related to analytical studies.

Probabilistic components

These components are utilized for finding out the probability distribution functions of various parameters included in the Bayesian model. As the model is used for predicting future values analogues to the present values, probabilistic distribution rules and other theorems related to this statistical study is important. Most importantly, the relation between two conditioned parameters is governed by the Bayes theorem. This has made the study of the conditional parameters more realistic and mathematical.

Graphical components

The Bayesian network is represented using graphs and nodes, something that has been derived from the theory of the graph trees. The nodes represent the variables that are used in that particular model and the tree edges represent the directional flow of dependency. The edges contain arrowheads, representing the direction of the control flow from one node to another. Even graphical structures are constructed using bar or column graphs to establish a surveyed study.

Directed acyclic graph

The Bayesian network is of the acyclic type with directed graph flow. This means that the starting and the ending node of the tree will be same, with each edge traversed only once. This is the directed flow graph of acyclic manner. However, if some nodes fall out of the tree and do not have any return path, then those nodes are considered to be out of the main graph tree.

Applications of the Bayesian network

The Bayesian network has found its applications in a number of fields and hence the analytical model system has proved to be quite effective. Some of the applications are listed here!

Weather forecasting

The interdependencies of each geographical parameter are established using a Bayesian network model. The weather officials use the joint PDF and other types of rules in order to predict the different weather details. For example, knowing the values of the temperature of an area, one can predict the pressure and also the wind velocity since both are interdependent. And both temperature and wind velocity will direct towards the amount of rainfall in that particular area.


Bioinformatics is the study of different information related to cell structure, genetic counting, and protein related information and so on. A statistical help in regulating the study of these parameters and also to establish the relation between them. Biologists can find ways to improve the conditions using the conditional probability theory; hence, the Bayesian network is quite useful in this particular field.

Document classification

Document classification is a process of separating and sorting of documental information based on certain parameters, like genres, paragraph styles, and so on. In earlier days, these types of classifications were done manually, which resulted in more time consumption. As a result, a system was developed that would help in the classification in a lucid manner. This is where the Bayesian network comes into the foreplay and hence, this probabilistic model is used to study different documents and establish the conditional directions.

Stock price prediction

For businesspersons who deal with stocks of materials and revenues, the Bayesian network has proven to be quite beneficial. Using a perfectly modeled Bayesian tree, one can establish the dependency of current parameters and the future stock price. This prediction is done mainly using daily stock price and the model is prepared on the network price of the stocks.

Artificial intelligence

As artificial intelligence is based on establishing different concepts of artificial brains and prediction of futuristic values. The use of analytical and statistical data is predominant in this field. As a result, the Bayesian network has found extensive application in the field of artificial intelligence. It is mainly used to relate the different probability parameters that govern the path of improvement of the existing artificial intelligent systems.

Data fusion

This is the technique used by many graphic designers, data imaging officials, and other field workers where they merge two or more relevant data columns and predict a more accurate and realistic data list. In this type of work, where the prediction probability is crucial, the Bayesian network has played an important role in bringing out the accurate data lists. As a result, the data fusion works have become easier and with much more ease.

Risk analysis

In this type of analysis, the Bayesian network is used to predict the risks related to a certain situation. The situation contexts are described in the form of parameters and once they are developed into a Bayesian model, one can easily know about the risk factors.

Limitations of the Bayesian network

We know that this modeling for the probabilistic and analytical study has become quite prominent in the technological field; this particular DAG model has a few limitations.

One cannot relate this network with complex real-life applications. This limitation has become more profound in the last few years since the application got limited when it came to real life statistical analysis. This hindrance has reduced the efficiency of the Bayesian network and therefore, it is used with other advanced network studies in order to bring out accurate results.

The network cannot provide reasoning for data collections where the numbers will come from different genres but they will be relevant enough. This has limited the use of a Bayesian network to simpler value instructions where the numbers are from the same background and of the same type.

Bayesian network demands that the present values should be accurate and more prominent for producing equally accurate future predicted results. However, it is not possible to build a collection of stats that will be based on 100% accuracy and hence the result of Bayesian network dwindles.

For data which are completely new to the Bayesian network knowledge, the probability of future occurrences is decided to be zero by the system itself. This has created a lot of contradictions around the globe regarding the usefulness of the Bayesian logic.

It can only be used for directed graphical trees and if a condition is not established between two variables, then the network automatically rejects them. This has made it difficult for analysts to study indirect data graphs using the Bayesian network.

This is not fit for analysing small-sized data sheets since its programmes were done in such a way so that the input data sheets to any Bayesian network would be intensively wide.

In this blog post, you learned about Bayesian Network. Stay tuned to Magoosh data science blogs for more!

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