Bayesian networks help in understanding how one element or event is dependent on other assigning probabilities. This network defines the involvement of one event in reshaping the other. In simple words, you can say that these networks determine the probability of an event changing the occurrence of other events. For instance, if you are late to work, your department’s head will warn you for being late. However, there might be various reasons for you to be late. You might be late because of traffic, or you might have overslept. No matter what the reason, it has a direct influence on your warning. You can represent the relationship between these events using the Bayesian Network.
What are Bayesian Networks?
You can represent Bayesian Networks or Bayes Networks as Probabilistic Graphical Model. This model will help you provide a probability about various activities and events with expert opinion and data. You can use Bayesian Networks for various tasks such as:
- Decision making
- Automated insight
- Anomaly detection
- Time series prediction
The Bayesian network model uses the law of probability and includes probability distributions to predict the links between nodes.
The graphical definition of Bayesian Networks is they consist of different Nodes. All these Nodes link with each other’s variables.
There are various nodes in Bayesian networks. These nodes represent different variables, such as gender, age, or height of a person. There might be multiple variables in a single node, for instance, in Bayes Server. Multi-variable nodes do not contain a single variable but include more variables. There are two types of variables: discrete and continuous. The structure of the Bayesian network includes nodes and links. We call this structure the structural specification. A Bayes Server consists of discrete and continuous variables.
If a variable has mutual exclusive states, it is a discrete variable, such as gender (male and female).
The continuous distribution of a model depends on more than one discrete variable. You can use continuous variables in Bayes Server.
Working Mechanism of Bayesian Networks
According to their connection to other variables, the Bayesian networks consist of different variables or nodes that align in a pattern or distribution. If a variable has a strong similarity to another variable in a pattern, it links closely together.
All the variables in a network influence every other variable. If you change one variable, the network changes every other variable. For instance, if you throw a stone in a pond, the rippling form in the pond will spread all over the place with a decreasing effect. This means the variable that is far enough will cause a smaller effect than other variables near the changing variable—all the other points or variables in the network.
The nature of the networks is self-assembling. It finds the best possible position of each variable depending on the connection to other variables. In the machine learning process, the model can learn the data structure and make possible connections. Bayesian Networks have a high prediction level and are sensible and strong in aligning the variables or nodes.
The working mechanism of Bayes Networks is different than the rules that the regression model follows. In Regression, if a single variable changes, all the other variables remain the same. However, that is not the case in Bayesian Networks. In a Regression model, all the variables have independent values and have no interrelationship. Still, we can use Regression for assumptions. However, in the real world, the case is different. All the variables are interrelated, and Regression does not follow this concept.
That is the reason many regression models are failing. These models are far from reality, excluding this model’s use in two major classes of analysis; policy-based research and experimental research. On the other side, Bayesian Networks will include the outcome of all the other variables when you are trying to estimate the impact of one variable. This approach is understandable and close to reality. You can easily analyze the effect of variables in a complex model.
How to Use Bayesian Networks?
When you are designing a Bayesian Network, you need to find the following components:
You should find the random variables in your model.
Define the conditional relationships of all the variables among each other.
Find the probability distributions of all the variables that you include in your model.
To design a model, you need the above three aspects. An expert can specify the values of these problems to design a model. However, you should estimate the probability distribution from the data. Estimating the graphical structure and probability distributions can be challenging, but you can utilize the data to estimate these values. After completing the Bayesian Networks model for the domain, you can find reasoning data and make better and informed decisions after understanding the relationship between multiple variables.
To find the probability distribution of the data, you can use this learning algorithm. For instance, you can estimate the distribution parameters of the variables by assuming Gaussian distribution. To obtain the reasoning of the event, you can use this model in the situation. For example, if you already know the event’s outcome, you can include random variables to estimate the causes of the events or how you can affect the outcome in the future.
Bayesian Networks’ role in a machine learning model is to fill the gap between different models, such as logistic, linear, etc. These networks, with the help of nodes and links, keep fast and simple models linked together. Furthermore, this model helps in linking the models that do not include probability models. This helps in predicting the possibilities, computing, and generating huge data. For instance, deep Bayesian neural networks process and offer rare and new opportunities to predict causalities.
These models also use nodes and Bayesian statistics to perform counterfactual analysis. For instance, what will be the change in the outcome if we adjust variable A? Counterfactual analysis enables us to predict the result utilizing the data that we are still unable to find with deep learning techniques.
We can understand the concept with the following example. Suppose we have a dataset of the job applicants, and we need to find the factors that affect their hire ratings. With the help of counterfactual analysis, we can find the importance of font parameters for the application’s eligibility.