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Rohan Kotwani
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  • New Orleans, Louisiana
  • United States
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Rohan Kotwani replied to Prashanth Southekal, PhD's discussion FFT on Time Series Data
"FBProphet uses a periodogram to model time series using these components. I'd take a look at their source code. https://github.com/facebook/prophet/tree/master/python/fbprophet"
Sunday
Rohan Kotwani commented on Rohan Kotwani's blog post New Algorithm For Density Estimation and Noise Reduction
"Peter, I'm happy to hear that you are going to test the algorithm on that contest. Good luck."
Apr 15
Peter Cotton commented on Rohan Kotwani's blog post New Algorithm For Density Estimation and Noise Reduction
"Interesting approach. I'm going to use this to enter a density contest prediction here."
Apr 13
Rohan Kotwani posted a blog post

New Algorithm For Density Estimation and Noise Reduction

KernelML - Hierarchical Density FactorizationThe purpose, problem statement, and potential applications came from this post on datasciencecentral.com. The goal is to approximate any multi-variate distribution using a weighted sum of kernels. Here, a kernel refers to a parameterized distribution. This method of using a decaying weighted sum of kernels to approximate a…See More
Apr 12
Rohan Kotwani's blog post was featured

New Algorithm For Density Estimation and Noise Reduction

KernelML - Hierarchical Density FactorizationThe purpose, problem statement, and potential applications came from this post on datasciencecentral.com. The goal is to approximate any multi-variate distribution using a weighted sum of kernels. Here, a kernel refers to a parameterized distribution. This method of using a decaying weighted sum of kernels to approximate a…See More
Apr 12
Rohan Kotwani commented on Stephanie Glen's blog post Choosing the Correct Statistical Test
"Is Cramer’s V a standard statistical test now?"
Apr 8
Chris Kachris and Rohan Kotwani are now friends
Nov 11, 2019
Rohan Kotwani commented on Paolo Lulli's blog post Difference between Gradient Descent and Newton-Raphson
"The images aren't displaying.."
Aug 4, 2019
Rohan Kotwani posted a blog post

Create Transformed, N-Dimensional Polygons with Covariance Matrix

The covariance matrix has many interesting properties, and it can be found in mixture models, component analysis, Kalman filters, and more. Developing an intuition for how the covariance matrix operates is useful in understanding its practical implications. This article will focus on a few important properties, associated proofs, and then some interesting practical applications, i.e., extracting transformed polygons from a Gaussian mixture's covariance matrix.I have often found that research…See More
May 30, 2019
Rohan Kotwani posted blog posts
May 26, 2019
Rohan Kotwani's blog post was featured

Create Transformed, N-Dimensional Polygons with Covariance Matrix

The covariance matrix has many interesting properties, and it can be found in mixture models, component analysis, Kalman filters, and more. Developing an intuition for how the covariance matrix operates is useful in understanding its practical implications. This article will focus on a few important properties, associated proofs, and then some interesting practical applications, i.e., extracting transformed polygons from a Gaussian mixture's covariance matrix.I have often found that research…See More
May 26, 2019

Profile Information

Job Title:
Data Scentist
Seniority:
Other
Short Bio:
Interested in camera automation, time series forecasting, kernel methods, and sequential models.
LinkedIn Profile:
http://www.linkedin.com/in/rkotwani
Interests:
Contributing

Rohan Kotwani's Blog

New Algorithm For Density Estimation and Noise Reduction

Posted on April 8, 2020 at 9:30pm 2 Comments

KernelML - Hierarchical Density Factorization



Continue

Create Transformed, N-Dimensional Polygons with Covariance Matrix

Posted on May 26, 2019 at 7:30am 0 Comments

The covariance matrix has many interesting properties, and it can be found in mixture models, component analysis, Kalman filters, and more. Developing an intuition for how the covariance matrix operates is useful in understanding its practical implications. This article will focus on a few important properties, associated proofs, and then some interesting practical applications, i.e., extracting transformed polygons from a Gaussian mixture's covariance matrix.

I have often found that…

Continue

High Density Region Estimation with KernelML

Posted on January 3, 2019 at 4:00pm 0 Comments

Data scientists and predictive modelers often use 1-D and 2-D aggregate statistics for exploratory analysis, data cleaning, and feature creation. Higher dimensional aggregations, i.e., 3 dimensional and above, are more difficult to visualize and understand. High density regions are one example of these N-dimensional statistics. High density regions can be useful for summarizing common characteristics across multiple variables. Another use case is to validate a forecast prediction’s…

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Generalized Machine Learning - Kerneml - Simple ML to train Complex ML

Posted on May 4, 2018 at 10:00am 2 Comments

I recently created a ‘particle optimizer’ and published a pip python package called kernelml. The motivation for making this algorithm was to give analysts and data scientists a generalized machine learning algorithm for complex loss functions and non-linear coefficients. The optimizer uses a combination of simple machine learning and probabilistic simulations to search for optimal parameters using a loss function, input and output matrices, and (optionally) a random…

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