In this article, I would be discussing the difference between marginalization and reduction in probability theory

If you ever have used Probabilistic Graphical Models (PGM) you might have come across marginalization and reduction of factors in probability space or in general many of these concepts are been introduced in probability theory. We often get confused between marginalization and reduction or rather most of us feel it is one and the same thing but it is definitely not the same. So how is it different? What is marginalization? What is Reduction? So all of this question’s answer will be addressed in this article

Let’s go through some definitions of various terminologies before diving into the difference

Factors

A factor is analogous to a function in programming i.e it has arguments and it returns a value. Now arguments in factor are random variables and return outcome value resulted from the argument variables. Conditional Probability Distribution (CPD) is one type of factor. …


In this article, I will be discussing various text information access modes (Push vs Pull & Querying vs Browsing)

In one of my articles, I had discussed the text data based retrieval systems and database retrieval systems (link). Now its time to discuss what are various modes to access text information. We all have come across certain software applications or used certain software applications in which we either input certain text to get something or the system prompts (recommends) you certain information about something (like some products). Let us take one simple example of the Amazon website. When we want to buy certain items or items from amazon we simply input the query and we get some items related to our query. Have you ever noticed that after you buy something amazon often recommends you buy other kinds of stuff related to your bought item? …


In this article, I will be discussing the difference between text retrieval and database retrieval.

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In this digital world, text data is everywhere, right from tweets in twitter to parsing some text in documents, everything is associated with the text. Many machine learning based products make effective use of this text data to make amazing technologies based on topics like sentiment analysis, topic modeling, relation extraction, etc.

As text data is everywhere, therefore, it is important for us to focus and create algorithms that can help us retrieve data in minimal time with optimal relevancy. For e.g, …

About

Abhishek Salian

ML Researcher | Data Science Practitioner

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