Pursod Ramachandran - Profile on Academia.edu (original) (raw)
Address: Toronto, Ontario, Canada
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Institute for Control Science, Russian Academy of Sciences
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Papers by Pursod Ramachandran
This paper will discuss the application of Markov chains in DNA sequencing. Specifically, we will... more This paper will discuss the application of Markov chains in DNA sequencing. Specifically, we will look into the discrete and continuous stochastic process properties that appear in the discrete state space of various nucleotide base pairings models. The focus will be two models: Jukes-Cantor (JC69), the Kimura (K80) and their extensions.
We explore two different SIR models, used to approximate and interpret the changes in a disease i... more We explore two different SIR models, used to approximate and interpret the changes in a disease infected population over a period of time. First, we look at properties of the epidemic model, its stability and how an epidemic affects a population over time. Then we look at the properties of the endemic model, discuss its stability and solutions over time. Finally, we use influenza as an example for a modified endemic model and discuss how to improve the model's accuracy.
This paper will discuss the application of Markov chains in DNA sequencing. Specifically, we will... more This paper will discuss the application of Markov chains in DNA sequencing. Specifically, we will look into the discrete and continuous stochastic process properties that appear in the discrete state space of various nucleotide base pairings models. The focus will be two models: Jukes-Cantor (JC69), the Kimura (K80) and their extensions.
We explore two different SIR models, used to approximate and interpret the changes in a disease i... more We explore two different SIR models, used to approximate and interpret the changes in a disease infected population over a period of time. First, we look at properties of the epidemic model, its stability and how an epidemic affects a population over time. Then we look at the properties of the endemic model, discuss its stability and solutions over time. Finally, we use influenza as an example for a modified endemic model and discuss how to improve the model's accuracy.