Research Blog
Insights and findings from statistical machine learning research, data science, and mathematical modeling.
Introduction To Non-Informative Priors
> Prior density is denoted by $g(.)$ in this article # Introduction Non-Informative Priors are the priors which we assume when we do not have any belief about the parameter let say $ \theta $ . This leads noninformative priors to not favor any value of $ \theta $...
Supervised Learning with Scikit Learn
Machine Learning is the art of giving computers the ability to learn from data and make decisions on their own without explicitly programmed for example - The determination of benign and malign according to the tumor size - Google News Selecting similar news and making a cluster of news...
Introduction to Logistic Regression
Usually in Linear Regression we consider $X$ as a explanatory variable whose columns are $X_1 , X_2 .....X_{p}$ are the variables which we use predict are the independent variable $y$ , we measure these values on a continuous scale,When the dependent variable y is dichotomous such as, Male or...
Highest Posterior Density Interval
Highest Posterior Density Interval is interval of the parmeter in which the posterir value are high when compared to any other point outside the interval (i.e. the posterior value is high in the interval). It can be defined as a 100(1-alpha)% HPD for a parameter $\theta$ is $\mathcal{C} =...
Convergence of Markov Chain
## What is Markov Chain ? Markov Chain is a Stochastic Model in which Future is dependent only on Present not on Past , What I mean to say that is $$ P(X^{t+1}|X^t,X^{t-1},...X^2,X^1) = P(X^{t+1}| X^t) $$ #### Transition Probability Matrix Let us denote $$ p_{ij} = P(X^{n+1} =...
Probability Theory (Series)
> This series is an introduction to Probability Theory, It closely follows the book "Probability Essentials" by Jean Jacod and Philip Protter. ## Day 0 : Philosphical Introduction to Probability Theory Lets Start with a Random Experiment, A random experiment is an experiment whose outcome is not predictable with...
fastkme : Faster Kaplan-Meier Estimator using JIT
The nonaprametric survival models like Nearest Neighbour, Kernel Survival, COBRA Survival or adaptive nearest neighbour require fitting kaplan meier estimator, while tuning these models the kaplan meier estimator is calculated thousands of time, this motivates us to create kaplan meier estimator which is faster than the existing one provided...