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Roles Played by Bayesian Networks in Machine Learning
As we all know, Machine learning is a hot and new buzzing topic of today’s era. It uses a certain form of statistical algorithms to make computers work. Input is provided to the algorithms and through statistical methods, the output is generated. Since machine learning is an application of AL and the purpose is to make machines capable of self-learning.
To make machine learning systems capable we need some of the things like data, to predict the output; Algorithms, to determine patterns; Automation, for automatic operation; Iteration, Scalability, Modeling etc. The methods of machine learning are divided into categories like Supervised learning, unsupervised learning and reinforcement learning. One can join the Machine learning course which covers all these topics and details on machine learning to achieve and upgrade the skills. It provides the in-depth knowledge of data preprocessing, regression, clustering, deep learning and other useful concepts to uncover the hidden insights of ML and make your analytical approach stronger.
Let’s begin understanding Bayesian network.
Bayesian Network is a probabilistic graphical model which comprises variables and its relationships. It uses Bayesian inference and learning to develop the algorithm. It is used to resolve the probabilistic queries. For instance, A network having probabilistic nodes and each node is associated with a probabilistic function which takes an input to give a possible value.
The Bayesian network represents probabilistic relationships of variables through Directed Acyclic Graph(DAG). Through the graph, the efficient inference is conducted on random variables with the help of factors. Bayesian statistics is dependent on the Theorem of Bayes. According to this theorem, it gives privilege to the user to update the probability of an unobserved event. Reverend Thomas Bayes stated the theorem of probability i.e Bayes theorem.
Before we start what exactly is BN, first understand the probability theory.
By chain rule, For a random variable B_0, B_1,…….., B_n the JPD or joint probability distribution will be P(B_0, B_1,…….B_n) and is equal to the P(B_1 | B_2,….,B_n) * P(B_2 | B_3,….B_n) * P(B_n).
For instance, A is taken in place of B, then in factorized form, it will be represented as:
P(A, B | C) = P (A|C) * P(B|C), is the conditional independence for the two random variables A and B and C is another variable. In short, A and B will be independent till C is fixed and known. In another form, it will be represented as P( A | B, C) = P (A | C).
A marked cyclic graph will be the Bayesian network. The graph show cases the joint probability distribution over a set of random variables.
For example, A patient is suffering from a particular disease. But to calculate, the other health related factors which may affect, we will examine the patient on those health issues, and to calculate the probability of other diseases we can use BN. Here, the equation compiling it is:
B = (G,θ)
Where, B represents the Bayesian network G represents DAG comprises X1, X2….Xn random nodes. θ represents the set of BN parameters. The set contains θ xi |πi = PB(xi |πi) parameter.
Then, BN can be stated as
From the equation we can understand that xi having parents and the probability distribution for it is a conditional while for other it is unconditional. There are two types of nodes are available, one is observable and another is unobservable variable. The observable variable is the evidence nodes and hidden or unseen variable are unobservable. The graph model follows a specific structure and parameters. The node parameters are defined in a conditional probability distribution(CPD). The discrete variables of CPD are defined in CPT or conditional probability table.
Example of BN:
If we want to determine the possibility of wetness or dryness on the grass. We will consider the weather in that case whether it is sunny or rainy. In the first case, again there are two cases either the sprinkler is on or off. And in the second, due to rain, the grass is wet.
Whatever is the probability of wetness, BN can calculate the probability of answer such as wetness of lawn is caused by rain or by the sprinkler. It covers the questions like if the probability of rain is increased, what will be the impacts? The probability depends on the two causes only: raining and sprinklers are open. To determine this we can use Bayes theorem, the probability of rain is the most probable reason behind the wetness of grass.
Apart from these, there are various machine learning tools available such as ai-one, TensorFlow, Protege, OpenNN, Veles, DiffBlue etc. These tools help the user to implement AI through software development kit, documentation, tutorials, open source network, and much more. Also, provide a platform for node interaction, advanced analytics by the use of python language. Some of the applications of BN are image processing, bioinformatics, and computational biology.