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Bayesian networks

What are Bayesian networks?

A Bayesian network is a kind of Probabilistic Graphical Model that makes use of Bayesian inference to carry out probability computations. 

They are probabilistic because they are created from probability distributions. They also employ the laws of probability for prediction and anomaly detection, reasoning, diagnostics, decision-making under uncertainty, and time series prediction.

Bayesian networks are also called Belief networks, Causal networks, and Bayes nets. You can use them to build models from data and/or expert opinions. They are used for modeling and reasoning with uncertain beliefs.

By representing conditional dependence by edges in a directed graph, they seek to model conditional dependence, thus modeling causation as well.

The relationships help you conduct inference on random variables in the graph by using of factors. The networks are directed acyclic graphs and every one of their edges corresponds to a conditional dependency, while every node corresponds to a unique random variable.

These networks satisfy the local Markov property which allows you to simplify the joint distribution to a smaller form. It helps you minimize the amount of computation needed in bigger networks.


What are the important components of Bayesian networks?

The two important components of Bayesian networks are the qualitative component, i.e. the Directed Acyclic Graph (DAG), and the quantitative component, i.e., the conditional probabilities.

How to create a Bayesian network?

To create a Bayesian network, you need to identify and define three things.

First, you need to define the variables that exist in the problem that you want to be solved and identify the main variable. 

After that, you need to define the conditional relationships between all the variables, i.e., the structure of the network.

Next, you need to figure out the probability distributions for each variable, i.e., the probability rules for the relationships between variables.

These steps can be carried out with data or expert opinions. They can even be done using both.

What are Bayesian networks used for?

Bayesian networks have a number of applications. Here are their most prominent uses:

1. Medical diagnosis

They can be used to figure out the probable disease that a patient is suffering from, based on the symptoms that are identified. A doctor can note the symptoms that are observed and enter them into the program which would compute the probabilities of multiple diseases based on the symptoms that were identified.


2. Testing hypotheses

Bayesian networks help us understand causal relationships. They help us understand whether the effect of a new feature is desirable. 


3. Environmental modeling

These networks can be used to model animal population trends. Environmental stressors have a lot of attention paid to them here.


4. Forecasting traffic 

Bayesian networks can be used to forecast traffic flows and learn from them.



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Bayesian networks

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What are Bayesian networks?

A Bayesian network is a kind of Probabilistic Graphical Model that makes use of Bayesian inference to carry out probability computations. 

They are probabilistic because they are created from probability distributions. They also employ the laws of probability for prediction and anomaly detection, reasoning, diagnostics, decision-making under uncertainty, and time series prediction.

Bayesian networks are also called Belief networks, Causal networks, and Bayes nets. You can use them to build models from data and/or expert opinions. They are used for modeling and reasoning with uncertain beliefs.

By representing conditional dependence by edges in a directed graph, they seek to model conditional dependence, thus modeling causation as well.

The relationships help you conduct inference on random variables in the graph by using of factors. The networks are directed acyclic graphs and every one of their edges corresponds to a conditional dependency, while every node corresponds to a unique random variable.

These networks satisfy the local Markov property which allows you to simplify the joint distribution to a smaller form. It helps you minimize the amount of computation needed in bigger networks.


What are the important components of Bayesian networks?

The two important components of Bayesian networks are the qualitative component, i.e. the Directed Acyclic Graph (DAG), and the quantitative component, i.e., the conditional probabilities.

How to create a Bayesian network?

To create a Bayesian network, you need to identify and define three things.

First, you need to define the variables that exist in the problem that you want to be solved and identify the main variable. 

After that, you need to define the conditional relationships between all the variables, i.e., the structure of the network.

Next, you need to figure out the probability distributions for each variable, i.e., the probability rules for the relationships between variables.

These steps can be carried out with data or expert opinions. They can even be done using both.

What are Bayesian networks used for?

Bayesian networks have a number of applications. Here are their most prominent uses:

1. Medical diagnosis

They can be used to figure out the probable disease that a patient is suffering from, based on the symptoms that are identified. A doctor can note the symptoms that are observed and enter them into the program which would compute the probabilities of multiple diseases based on the symptoms that were identified.


2. Testing hypotheses

Bayesian networks help us understand causal relationships. They help us understand whether the effect of a new feature is desirable. 


3. Environmental modeling

These networks can be used to model animal population trends. Environmental stressors have a lot of attention paid to them here.


4. Forecasting traffic 

Bayesian networks can be used to forecast traffic flows and learn from them.



Thanks for reading! We hope you found this helpful.

Ready to level-up your business? Click here.

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