# The Bayesian Belief Network in Machine Learning

Machine learning, artificial intelligence, big data – these up-and-coming technologies are practically buzzwords at this point. They show more promise to change the world as we know it than most of the things we’ve seen in the past, with the only difference being that these technologies are already reshaping the digital landscape.

Through the digital revolution, a lot of aspects of our collectivized lives have changed forever. Automation is the future, and machine learning and artificial intelligence solutions only work to enable it. A machine doesn’t need breaks, rest, or leave – making it an exemplary employee for menial tasks.

Don’t worry – machines aren’t going to steal our jobs – they’ll make them better in more ways than we can imagine. But, even if highly beneficial, these technologies are more complex than one might imagine.

One of the things that puzzle a lot of people is the Bayesian Belief Network. Every article so far written on the matter handles it in a very confusing and technical way. Below, we’ll solve that issue by merely presenting this framework.

## What is the Bayesian Belief Network in Machine Learning?

The Bayesian Belief Network (BBN) is a crucial framework technology that deals with probabilistic events to resolve an issue that has any given uncertainty. A probabilistic graphical model visually presents variables and their unique dependencies through a directed graph with no directed cycles (DAG).

In layman’s terms, the BBN presents conditional dependencies between two or more completely arbitrary variables.

These networks are solely probabilistic, and they’re used to detect potential anomalies. This makes them extremely useful for application in machine learning, which relies heavily on anomaly detection.

The Bayesian belief network isn’t a new thing, and machine learning isn’t the only thing that utilizes this network. Its application and use cases are vast, as this network is used to:

- Predicting based on historical data;
- Detecting possible data anomalies;
- Diagnosing existing issues;
- Providing automatic insight;
- Logic and reasoning within AI;
- Binary decision making when exposed to unpredictability;

Aside from machine learning, the Bayesian Belief Network is used in AI, Big Data, and Data Mining and is a crucial data science aspect.

**What Are the Data Requirements? **

The Bayesian Belief Network excels in its applicability, meaning it’s able to operate with virtually any data. As long as there are variables between data, the Bayesian Belief Network can be applied to it. We can utilize the data and detect any probabilities through this method, thus allowing the data to be furthered into machine learning.

Think of the Bayesian Belief Network as a necessary stepping stone in data refinement, making it available for further application and analysis.

## Defining Bayes’ Theorem

The Bayesian Belief Network is solely reliant on one mathematical theorem, which is the Bayes’ Theorem. The Bayes’ theorem is crucial in statistics and probability, as it allows for accurate assessment of a given situation through the use of available data.

The Bayes’ theorem is a cornerstone of Bayesian statistics, which is another crucial theorem in statistics that operates through degrees of belief.

In machine learning, Bayes’ theorem serves as a crucial aspect of probability as a whole. It’s done through calculation, which takes the posterior probability of a given hypothesis into account by multiplying it with the actual likelihood and subsequently dividing it by the probability of seeing the actual data itself.

## How is the Bayesian Belief Network Applied in Machine Learning?

Machine learning is all about probability, making the Bayesian Belief Network applicable to more than a few aspects of machine learning as a whole. BBN’s primary role in machine learning is to visualize the probability model for any given domain, assess and reason probabilities for any given scenario with factual data and evidence, and oversee the connection between the many random variables in any given situation.

These networks offer a visual probability model, which is expressed through a graph that’s defined by data nodes and orientated edges. The BBN determines the dependencies and dependencies between selecting given variables in any given sheet of data through this probability model, allowing for clarification, estimations, and decision-making in the machine learning process.

Each domain variable is defined by its node, and each node is defined through arcs. The nodes in question are all quantified by a conditional probability distribution table, which is a prearranged set of integrated random variables.

The BBN models are the defining factor of the theorem itself, and their preparation defines the success rate of the theorem itself. The more data, the better the data, and the more data derivatives, the better the BBN probability event estimation.

The Bayesian Belief Network is instrumental in machine learning, as it substantiates almost every step of the way, which includes data pre-processing, actual learning, and post-processing. The BBN is used in the unsupervised construction of deep neural networks through structure learning, which is directly enabled by applying the generative graph through Bayes’ theorem. The algorithmic nature of this application makes it suitable for machine learning – and through the improved structuring of data, BBN plays an even more prominent role in the machine learning process.

## Final Thoughts

Applied machine learning is exciting, but it’s a tricky thing to comprehend. Machine learning itself has more applications than merely enabling artificial intelligence, as it’s a crucial technology that is a cornerstone of data science as a whole. Machine learning itself is quite complex, as many things enable it, one such being the Bayesian belief network.

While not everyone will be able to comprehend the BBN in all of its mathematical and scientific glory, we hope that this article has cleared up any misconceptions and questions you’ve had about the theorem and network understandably.