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Prototypes and Models | Applied Sciences | India | Volume 13 Issue 1, January 2024

An Applications in Computational Biology and Evolution of Hidden Markov Model based on Assumptions of Coding Theory

Amitesh Kumar Singam ^[4] | Venkat Pashike | Muhammad Shahid ^[2]

Abstract: In General, assumptions of knowns or unknowns i. e, observation based on consistency or inconsistency within Channel coding i. e, in some cases, during rate distortion optimization process as mentioned in [2], [3], [1] it states that inconsistency may leads towards extreme state of saturation point within transmitted data due to evolution within channel capacity, moreover assumptions of probability based on possibilities sometimes results in inconsistency that may develop an error in coding distortions which is referred as Loss or Cost function that may lead towards activation of translations within in channel with respective Language encryption. Moreover in Science theory, Hypothetical Assumptions, i. e, beyond the state of Possibility, the accuracy towards understanding the observations of any observer within Assumptions of Hidden Markov Model based on probability factor will define consistency in observations. Finally, we concluded that our approach based on development of channel capacity due to inconsistency within observations and inclusion of loss function due to error in functionality may activate translations which may fall into the section relevant to applications in computational biology.

Keywords: Hidden Markov Model, Rate Distortion Optimization, Computational Biology

Edition: Volume 13 Issue 1, January 2024,

Pages: 1676 - 1677