If you have fiddled around enough with C/C++/Objective-C, you must have noticed that if you use rand() function on its own, it gives the exact same numbers every time. Now how is that possible? Isn't rand() function supposed to generated completely random numbers in a given range? The reason for this has something to do … Continue reading Random Number Generators In Programming
Tag: Probability
Gaussian Mixture Models
Let's say you have a lot of data and you want to estimate the underlying statistical model. Wait a minute, why on earth would I care about that? Well, if you estimate the model, then you can analyze unknown data that is not under our control. Some of the common examples would be weather estimation, … Continue reading Gaussian Mixture Models
What Are Conditional Random Fields?
This is a continuation of my previous blog post. In that post, we discussed about why we need conditional random fields in the first place. We have graphical models in machine learning that are widely used to solve many different problems. But Conditional Random Fields (CRFs) address a critical problem faced by these graphical models. … Continue reading What Are Conditional Random Fields?
Why Do We Need Conditional Random Fields?
This is a two-part discussion. In this blog post, we will discuss the need for conditional random fields. In the next one, we will discuss what exactly they are and how do we use them. The task of assigning labels to a set of observation sequences arises in many fields, including computer vision, bioinformatics, computational … Continue reading Why Do We Need Conditional Random Fields?
Asymmetric Dominance Effect
Let's consider a situation. There is a company with two products, A and B. Both these products have their own merits and demerits. Product A has relatively less features, but it's price is low. Product B, on the other hand, has more features but it's more expensive. Consumers tend to pick both these products depending … Continue reading Asymmetric Dominance Effect
Expectation Maximization
Probabilistic models are commonly used to model various forms of data, including physical, biological, seismic, etc. Much of their popularity can be attributed to the existence of efficient and robust procedures for learning parameters from observations. Often, however, the only data available for training a probabilistic model are incomplete. Missing values can occur which will … Continue reading Expectation Maximization
Bayesian Classifier
In machine learning, classification is the process of identifying the category of an unknown input based on the set of categories we already have. A classifier, as the name suggests, classifies things into multiple categories. It is used in various real life situations like face detection, image search, fingerprint recognition, etc. Some of the tasks are … Continue reading Bayesian Classifier
The Butterfly Effect
This blog post is a continuation of my previous post on Chaos Theory. Although it is not required for you to read that post to understand this post, it would be better if you glance through it once. All of us have heard about the Butterfly Effect. It is one of the very famous examples … Continue reading The Butterfly Effect
Chaos Theory
Chaos Theory is a mathematical sub-discipline that attempts to explain the fact that complex and unpredictable results can and will occur in systems that are sensitive to their initial conditions. Some common examples of systems that chaos theory helped understand are earth's weather system, the behavior of water boiling on a stove, migratory patterns of … Continue reading Chaos Theory
Probabilistic Randomness Of Stochasticity
Do you see what I did with the title there? Anyway, you must have heard the term 'probability' being used around you. People use it in different contexts and in different forms - "What is the probability that Spain will win the next world cup?" or "I will probably finish reading the book by midnight" … Continue reading Probabilistic Randomness Of Stochasticity
Perpetual Enigma 