If you have studied probability theory, then you must have heard Markov’s name. When we study probability and statistics, we tend to deal with independent trials. What this means is that if you conduct an experiment a lot of times, we assume that the outcome of one trial doesn’t influence the outcome of the next trial. For example, let’s say you are tossing a coin. If you toss the coin 5 times, you are bound to get either heads or tails with equal probability. If the outcome of the first toss is heads, it doesn’t tell us anything about the next trial. But what if we are dealing with a situation where this assumption is not true? If we are dealing with something like estimating the weather, we cannot assume that today’s weather is not affected by what happened yesterday. If we go ahead with the independence assumption here, we are bound to get wrong results. How do we formulate this kind of model? Continue reading

# Tag Archives: Statistics

# What Is Maximum Likelihood Estimation?

Let’s say you are trying to estimate the height of a group of people somewhere. If the group is small enough, you can just measure all of them and be done with it. But in real life, the groups are pretty large and you cannot measure each and every person. So we end up having a model which will estimate the height of a person. For example, if you are surveying a group of professional basketball players, you may have a model which will be centered around 6’7″ with a variance of a couple of inches. But how do we get this model in the first place? How do we know if this model is accurate enough to fit the entire group? Continue reading

# What Are Confidence Intervals?

Confidence interval is a concept in statistics that is used extensively in many diverse areas like physics, chemistry, computer vision, machine learning, genetics, etc. This concept is so fundamental that any modern science would eventually end up using it. Let’s say you have collected some data and you want to understand the behavior of that data. For example, you can say that the data is centered around some value or that the data is distributed with a certain amount of variance. This is very common in many fields where you have estimate the underlying parameters that govern the data distribution. When you estimate a statistical parameter from some data, you can’t be certain about its true value. If you have a lot of high-quality data, then you’re more confident that your estimate is near its true value. But if you don’t have a lot of data, or if it’s of poor quality, then you don’t have much confidence in it. So how do we deal with these situations? Can we measure this uncertainty? Continue reading

# What Are P-Values?

Let’s say you are a part of the sub-atomic physics team and you are working on discovering an important effect. The thing about sub-atomic physics is that nothing is certain and you cannot say something has happened with 100% certainty. The best we can do is to say that we are x-percent sure that something interesting happened. One fine day, you see some pattern in your data which looks pretty much like what that effect would look like. Now the problem is, your experiment produced data with a lot of noise. People are therefore skeptical of you, and think that the supposed “effect” you claimed to see might just have been a funny pattern in some random noise. How would you convince them that it’s not? Before that, how do you convince yourself that it’s not just noise? A good strategy for arguing your point would be to say, “Alright listen, suppose you’re right, and the patterns in my data really are in fact just from random noise, then how would you explain the fact that random noise very rarely produces patterns like this?”. Pretty good strategy right? Now how do we formulate this mathematically? Continue reading

# 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, facial expressions analysis, speech recognition, share prices, etc. Coming back to the estimation problem, the simplest thing to do would be compute the mean and variance of this data, hence getting the governing distribution. But what if there are multiple subgroups in this data? As in, how do we detect the presence of subpopulations within an overall population? Even though the data points belong to the overall data, we need to understand the different modes inside the data. How do we go about doing this? Continue reading

# Overfitting In Machine Learning

Let’s say you are given a small set of data points. These data points can take any form like weight distribution of people, location of people who buy your products, types of smartphones, etc. Now your job is to estimate the underlying model. As in, if an unknown point comes in, you should to be able to fit it into your model. Typical supervised learning stuff! But the problem is that you have very few datapoints to begin with. So how do we accurately estimate that model? Should you really tighten your model to satisfy every single point you have? Continue reading

# 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 really simple and a machine can identify the class with absolute certainty. A common example would be to determine if a given number is even or odd. Pretty simple right! But most of the real life problems are not this simple and there is absolutely no way a machine can identify it with absolute certainty. For example, object recognition, weather prediction, handwriting analysis etc. So how do machines deal with these problems? What approach can be used here? Continue reading