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What is a Bayesian Neural Networks? Background, Basic Idea & Function

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23rd Dec, 2020
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What is a Bayesian Neural Networks? Background, Basic Idea & Function


This article deals with the fundamental concept of Bayesian Neural Networks. This particular concept of Bayesian Neural Networks comes to play when unseen data is fed into the neural network, creating uncertainty.

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The measure of this uncertainty in the prediction, which is missing from the Neural Network Architectures, is what Bayesian Neural Nets explains. It tackles overfitting but also helps in the additional features such as estimating uncertainty and the probability distribution. The concept of Neural Networks have been explained as well.

Read: Types of Supervised Learning

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Background of Bayesian Neural Network

Comparing one of the best performing artificial intelligence systems of the last ten years, all of these machines have one thing in common- them incorporating a sophisticated technique called Deep Learning.

Cutting open the concept of Deep Learning, it can be observed that it is a name which allowed for a relatively new approach to Artificial Intelligence, called the Neural Networks, which have been inconsistently in trend for about more than 70 years. As an example, it is observed that the concept of Neural Nets is vaguely based on the human brain, which consists of millions of processing nodes connected as a dense mesh just as intertwined wires.

The Bayesian Neural Networks, hence, conveniently deal with the issue of uncertainties in the training data which is so fed.

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Basic Idea of Bayesian Neural Network

Neural Networks, more popularly known as the Neural Nets, is an effective way of Machine Learning, in which the computer learns, analyzes, and performs the tasks by analyzing the training examples. The examples used are mostly labeled by hand in advance. For instance, take an object recognition system.

The information regarding labeled images of automobiles, cars, houses, or any object is given or fed. It then formulates a logical inference in the visual pattern inserted as data that shall be consistently correlating with other specific labels.

The Architects that work with Neural Nets have been victorious in breaking down and learning of very intricate input and output mappings from the data. Nevertheless, a basic knowledge of the same input and the output mapping system usually ends up not satisfying most of the situations, especially when there is a need for the integration of belief of a particular model or in circumstances where data is limited. 

The Bayesian Neural Networks are those criteria or parameters that are under most circumstances expressed as distribution and are usually learned through the concept of Bayesian Inference, as compared to a deterministic value. They have an inner ability to digest the complex, non-linear function from the data and then to express the uncertainties- both at the same time. It has hence also led them to a higher role in the pursuit to garner and build a more reliable and competent AI. 

Must Read: Types of Regression Models in Machine Learning

What Are Bayesian Neural Networks?

Hence, Bayesian Neural Network refers to the extension of the standard network concerning the previous inference. Bayesian Neural Networks proves to be extremely effective in specific settings when uncertainty is high and absolute. Those circumstances are namely the decision-making system, or with a relatively lower data setting, or any kind of model-based learning.

The Deep Neural Networks (DNN) tend to formulate a logical inference with the given data without having any prior experience with the set of data. As a result, they perform exceptionally well with data that is non-linear by nature and therefore, require a large amount of data for the sole training purpose. Due to the loading up of more information, the problem of overfitting surfaces.

The quandary which arises in the present situation is that the neural nets, as observed before, work exceptionally well with the data that is fed for the sole purpose of training but will tend to underperform when new and foreign data is fed into the system. This leads to the Nets being blind to certain uncertainties in the training data itself, leading them to be overconfident in their predictions, which can be misleading. To do away with errors such as these, the Bayesian Neural Networks are therefore used.

How Does Bayesian Neural Nets (BNN) Work?

The main object and idea behind the Bayesian Neural Networks are that every unit is in association with the probability distribution, which includes the weights and the biases.

They are known as the random variables, which will be providing a completely different value at each time when it is accessed. 

Taking an example, if X is a variable and is a completely random variable, represents the normal distribution. Each time X is accessed, a divergent value of X is given. The process for obtaining a diverging value of each time the value of X is retrieved, is called Sampling. The value that is derived from every sample is dependent on the probability distribution.

As the ambit of probability distribution rises, the uncertainty is directly proportional; as a result, it rises as well. Typically in a neural network, every layer shall have weights that are fixed, with the biases that usually give an account of the output. A Bayesian Network, on the other hand, will have the probability distribution that will be attached to the layer itself. 

A multiple forward pass each time is performed, with a new set of weights as well as biases. It is hence used to deal with the issue of classification. The output is provided for every pass made in a forward manner. The data uploaded as an input image is what leads to heightened uncertainty. In such a case, it is an image that the net has not encountered before for the output classes.

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It is safe to conclude that Bayesian Neural Networks are a blessing when it comes to integrating and dealing with uncertainties. They also have manifested to improve prediction performances.

The primary foundational problems that occur in the development of Bayesian Neural Network or any model based on probability is the intractable computations of the previous distribution and their respective expectations. Moreover, it is exceptionally lucid that the problem of overfitting is very much robustly dealt with by the Bayesian Networks.

If you’re interested to learn more about machine learning, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.


Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
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Frequently Asked Questions (FAQs)

1How do you make a graphical model out of a Bayesian network?

By linking all of the nodes involved in each component, a Bayesian network may be turned into an undirected graphical model. This necessitates the joining of each node's parents. A moral graph is an undirected graph that corresponds to a specific Bayesian network. Computing the moral graph is the first stage in many Bayesian network computational techniques.

2What is the relationship between a Bayesian network and probability?

A Bayesian network is created using an acyclic directed graph. A Bayesian neural network is a probability model which is factored by applying a single conditional probability distribution for each variable for the given model. The distribution is based on the parents in the graph. The variables in the graph that are separated are still independent, but the undirected graph's basic graph separation is substituted with the more difficult d-separation, which takes into consideration the influence of competing explanations for observed values.

3Mention any one assumption that Bayesian networks make?

When we are provided with the target classification, a critical assumption for naive Bayesian classifiers is that all variable values are conditionally independent. This assumption greatly aids in simplifying the objective function computations in terms of posterior probability. However, for some applications, such as text documents and speech signals, this assumption may not be correct. Bayesian belief networks may be a useful choice in this scenario. They employ a set of conditionally independent probabilities rather than impose all possible values on all variables.

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