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This post is a spotlight interview with Jhonatan de Souza Oliveira on the topic of Bayesian Networks.

## Could you please introduce yourself?

My name is Jhonatan Oliveira and I am an undergraduate student in Electrical Engineering at the Federal University of Vicosa, Brazil. I have been interested in Artificial Intelligence since the beginning of college, when had my first adventure investigating and building a simple chatbot for a Symposium website. I also am a member of an autonomous robot soccer team called “BDP – Believe, Do and Play”, where we research and develop technologies for the RoboCup category called “Small Size League”.

In late 2012, I got a scholarship for a exchange program through a Brazilian government program called “Science Without Borders“. I went to University of Regina, where I could have the pleasure of meet Dr. Cory Butz, a well known Bayesian Network researcher.

Since then, I have been developing research in Bayesian Networks inference and modeling with Dr. Butz and a Brazilian friend, Andre Evaristo.

## What are Bayesian Networks?

In general, Bayesian Networks (BNs) is a framework for reasoning under uncertainty using probabilities. More formally, a BN is defined as a Directed Acyclic Graph (DAG) and a set of Conditional Probability Tables (CPTs). In practice, a problem domain is initially modeled as a DAG. Lets take an example from the good reference Bayesian Networks Without Tears (PDF):

Suppose when I go home at night, I want to know if my family is home before I open the doors. From my knowledge, I can model a DAG with the following information: usually, when my family is out, the outdoor light is on and the dog is out, but the dog is also out when it has bowel problems. If the dog is out I can hear its bark. This is the DAG for this problem, where the arrows indicate a causal idea:

From the probability side, we would need a Joint Probability Distribution (JPD) – in practice, for finite and discrete variables it would be a big table where each row associate a configuration of the variables’ domain to a probability value. In our example, all 5 variables are binary and the JPD would have 2^5 = 32 rows, which may not sound now a big deal but in more complex problems it can be intractable.

The intractability of the JPD was solved by Judea Pearl in a series of papers published since 1988. He proposed the use of conditional independences to break that big JPD table into small ones (yes the CPTs).

In our example, we only need to provide 5 small tables, which in total will sum to 2^2 + 2^1 + 2^3 + 2^1 + 2^2 = 20 rows.

After the problem domain is modelled with a DAG and a CPTs, we want to run some inference algorithm in order to update the model after absorbing some new evidence, say the light is on; or even answer some query about the problem, say what is the probability of hear a bark given that the light is on?

## Why do we need Bayesian Networks?

Humans are not good with reasoning in systems with limited or conflicting information. Consider a web search engine where the user type in a query and the system provides a list of results. Which web page is more relevant to this specific user? Now, consider a medical diagnosis system, in which a patient has some, but not all, of the symptoms of a disease.

It would be handy if we have something to manage all this limited/conflicting information. So, here is why we need them: BN is a framework for uncertainty management.

## What are some popular examples where Bayesian Networks were used?

BNs has been used in many fields, including improvements on propulsion systems, maintenance system and diagnostic analysis, among many others.

The paper “Display of Information for Time-Critical Decision Making” shows how NASA used BNs to manage “*the complexity of information displayed to people responsible for making high-stakes, time critical decisions*“.

Microsoft have used BNs to build a assistance software able to help users when they get stuck using Windows. The Lumiere Project, as it’s called, was used in the Office Assistant in Microsoft Office ’97 suite and was an intelligent user interface able to model a problem domain given the user’s background, actions and queries.

The heart disease program at MIT has as goal “*to assist physicians in the diagnosis of patients with cardiac symptoms, focusing on hemodynamic dysfunction*“. BNs are used to update the beliefs’ given the symptoms, and then inference is used to get the most probable diagnosis.

## Why aren’t Fuzzy Logic and Rule based Systems good enough reasoning systems?

Fuzzy Logic is not always intuitive. For example, consider flipping a fair coin, where it lands on heads has 0.5 as an assigned value and lands on tails has 0.5. Now, what is the belief that a given coin flip will land on heads “or” tails? According to fuzzy logic, two predicates related to an “or” operator has as result the minimum of both predicates assigned value, in our example, it would be 0.5 or 50%. Although we are 100% sure that the coin will land on heads “or” tails.

On the other hand, ruled based systems have a lack of semantics. A confidence-factor is used to measure the uncertainty in a set of “if-then” structures. For instance, consider a case where “*If the website does not open then I lost my internet connection – with a coefficient of 9*“. One could ask why the coefficient 9, or why not 19 or 900, or even ask if 9 is high or low. Yet, ruled based systems can become exponentially big trying to capture all conditions in a domain problem, in some cases becoming infeasible. For instance, if the website does not open my belief in “lost connection” becomes high, although we are not checking the possibility of a server problem or even a browser error.

## What are some limitations with Bayesian Networks?

Probably the most notable weakness of BNs is the designing methodology.There is no standard way of building BNs.

The design of a BN can be a considerable amount of effort in complex systems and it is based on the knowledge of the expert(s) who designed it. Although, this disadvantage can be good in another point of view, since BNs can be easily inspected by the designers and has the guarantee that the domain specific information is being used.

## What resources would you recommend to a beginner in Bayesian Networks?

For a beginner in BN (but with some AI knowledge), I would start with the excellent Bayesian Networks without Tears (PDF).

For textbooks on Bayesian Networks, I recommend:

- Probabilistic Graphical Models: Principles and Techniques
- Expert Systems and Probabilistic Network Models
- Modeling and Reasoning with Bayesian Networks
- Introduction to Bayesian Networks

An excellent academic resource is the Association for Uncertainty in Artificial Intelligence (AUAI). And, of course, Judea Pearl website is a rich resource for BNs stuff.

Great article. Could you explain how you arrived to the below.

2^2 + 2^1 + 2^3 + 2^1 + 2^2 = 20 rows

Hi, Emek! Thanks!

Good question, indeed. I didn’t get into details in that one, sorry about that.

When you’re modelling a BN, the CPTs you need are defined as the conditional probability of the children given the parents (we can prove this using the chain rule and the independence between variables), that is P( Child | Parent ).

In our running example, our needed CPTs are: P ( hear-bark | dog-out ), P( dog-out | family-out, bowel-problem ), P( bowel-problem ), P( family-out ) and P( light-on | family-out ).

Considering our variables as binary, we get tables of the size: 2^number-of-variables. Thus: 2^2 + 2^3 + 2^1 + 2^1 + 2^2.

Hope it’s clearer now.

Let me know.

Hi Jhonatan,

Good article.

Would you recommend the Python package Scikit-Learn to deal with Bayesian Networks?

Hope to hearing back from you.

Hi, Marcos.

I’ve never used Python Scikit to BN activities, though I know it’s a very interesting tool.

BN researchers usually end up building their own software, and that makes difficult to have a well known packaged in the area.

But I’ve heard a lot about this one, called JavaBayes ( http://www.cs.cmu.edu/~javabayes/index.html ), though I’ve never used it neither.

It’s open source under GNU License.

Thanks, Marcos.

Great article. I’m building my first BN, and appreciate the thoughtful guidance!

I’m glad to hear it Siobhán.

Hello, would you recommend a free software to model bayesian network. Software like agenarisk,netica an so on are very expensive and their trial versions useless. If your recommendation its weka ¿Where I can find good tutorials in weka/bayesian networks?.Thanks.

Sorry, I can’t. I hope to coverer bayes nets in detail in the future.

Very good article. Bayesian Networks are a really powerful tool in any artificial intelligence practitioner. Are there any good resource or library to get started with Bayesian Networks? Something like sklearn, for instance.

Thanks in advance for your time and attention!

Check out PYMC3

Re tools for Bayesian Networks: you might want to give Hugin a try. There are options to have it for free (through their website), its reach on functionality, and has APIs to various programming languages (Python, Java, C#, …).

Hope it helps someone to further explore the extremely exciting Bayesian Networks 🙂

P.S.

For Python in particular PyBayes seems to also cover this topic, though I didn’t try it (so far), and hence can’t really judge about its usefulness.

Thanks.

Very good article. Well, I agree with Jesús Martínez Bayesian Networks really powerful tool in any artificial intelligence practitioner.

Thanks.

I have few questions on the BN. Can you help on it?

This might help:

https://machinelearningmastery.com/introduction-to-bayesian-belief-networks/

Hi Jason,

I am very grateful to have your fantastic blog as a tutor.

I am going to start searching about “uncertainty in deep learning”, and I saw your blog as ever.

would you mind please giving me some tutorial video, book or website to start learning about “uncertainty in deep learning”?

Honestly, I got confused with many tutorials on the internet, so I will be thankful if you offer me some useful ones.

thank you in advance.

Maryam

What do you mean by “uncertainty in deep learning”?

Do you mean the stochastic nature of the learning algorithm:

https://machinelearningmastery.com/stochastic-in-machine-learning/

Or do you mean uncertainty in the predictions, e.g. predicting a probability:

https://machinelearningmastery.com/how-to-make-classification-and-regression-predictions-for-deep-learning-models-in-keras/