What is Kalma Filter
The Kalman Filter is a mathematical algorithm used for estimating and predicting the state of a dynamic system, particularly in the presence of noisy or uncertain data. It consist of five equations.
10/29/23About 13 min
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What is Kalma Filter
The Kalman Filter is a mathematical algorithm used for estimating and predicting the state of a dynamic system, particularly in the presence of noisy or uncertain data. It consist of five equations.
10/29/23About 10 min
Requirements
Instructions
Instructions
Use Rmarkdown to do the following tasks(2). Please note that the presentation of the document and the range of Rmarkdown features/functions used are matter.
10/20/23About 4 min
Requirements
Instructions
Instructions
Use Rmarkdown to do the following tasks(2). Please note that the presentation of the document and the range of Rmarkdown features/functions used are matter.
10/20/23About 8 min
10/2/23About 9 min
Requirements
Instructions
Instructions
Requirements Analysis
Suggestions for Topics for Projects
- Markov Chain Monte Carlo Methods
- Value at Risk and Conditional VaR
- Stochastic Differential Equations
- ARCH-GARCH with non-Normal errors.
- Applications of ARCH, GARCH
- Kalman Filter
- Extreme Value Distributions
- Market Microstructure
- Artificial Neural Network Models
- Machine Learning
- Genetic Algorithms
- Derivatives
- Weather Derivatives
- Applications of Markov processes
- Cellular automata – specifically the game of life
- Adaptive Least Squares
- Any other topic after discussion with me
10/2/23About 2 min
9/22/23About 3 min
Requirements
Instructions
Instructions
Requirements Analysis
8/25/23About 8 min
Requirements
Instructions
Instructions
Requirements Analysis
8/25/23About 2 min
Requirements
Instructions
Instructions
Dataset
BRCA-50 is a Breast cancer dataset, including the expression levels of 50 important genes in Breast cancer.
- The dataset includes 1212 samples with
- 112 samples are of normal cases (class = N) and
- 1100 samples are of cancer patients (class = C).
8/25/23About 2 min