#hidden markov model

[rodgermalcolmmitchell]

The cause of inflation down to the last decimal point WAG.

Years ago, I  took over a commodity brokerage with an employee who recently had won a chartist competition. A chartist is a securities researcher or trader who analyzes investments based on past market prices and technical indicators. He had endless historical data and formulas for that data, and based on all that, he predicted the markets. Despite winning a national competition, his trading…

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[machine-unlearning]

You know what, screw you *unhides your Markov model*

[ijtsrd]

Enabling Air Pollution Prediction through IoT and Machine Learning

by Suraj Kapse | Akshay Kurumkar | Vighnesh Manthapurwar | Prof. Rajesh Tak "Enabling Air Pollution Prediction through IoT and Machine Learning"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020,

URL: https://www.ijtsrd.com/papers/ijtsrd30739.pdf

Paper Url :https://www.ijtsrd.com/engineering/information-technology/30739/enabling-air-pollution-prediction-through-iot-and-machine-learning/suraj-kapse

callforpapermedicalscience, medicalsciencejournal, manuscriptsubmission

Large scale industrialization and the increase in the number of factories and industries across major cities in the world have been contributing to the decreasing air quality. This is since a rapid increase in the population across the world has prompted the majority of the companies across the globe to adopt mass production activities to keep up with the increasing demand. This is evident in the fact that most of the big cities have an increasing number of cases of respiratory illnesses and asthmatic symptoms in the populous. Therefore, there is an urgent need to address these issues to provide a better environment and reduce such incidences. The Internet of Things or IoT platform is a quite a promising platform for this approach which has been getting increasingly affordable and approachable. Therefore, in the approach stipulated in this research, the IoT platform has been utilized in addition to the Machine Learning paradigms to achieve accurate air quality predictions. The proposed methodology utilizes K nearest neighbors and Linear Regression, along with the Hidden Markov Model for effective Pollution level estimation. 

[altanai]

Markov model

In probability theory, a Markov model is a stochastic model used to model randomly changing systems.

Stochastic process

Let’s see what Stochastic process is first .

Stochastic is, having a random probability distribution or pattern that may be analysed statistically but may not be predicted precisely., or in simple terms refers to a randomly determined process . In probability theory and related…

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[monohedron]

[dataandme]

“Getting Started with Markov Chains” 

by Joseph Rickert via Rbloggers

There are number of R packages devoted to sophisticated applications of Markov chains. These include msm and SemiMarkov for fitting multistate models to panel data, mstate for survival analysis applications, TPmsm for estimating transition probabilities for 3-state progressive disease models, heemod for applying Markov models to health care economic applications, HMM and depmixS4 for fitting Hidden Markov Models and mcmc for working with Monte Carlo Markov Chains. [keep reading]

[ayraethazide]

Hidden Markov models (HMMs) are probabilistic statistical models which are useful in bioinformatics - particularly analysing protein and nucleotide sequences. The Markov component means that the identity of the system's following state depends only on the transition probabilities of the current state of the system. The hidden component means that the identity and nature of the state cannot be directly observed - instead, each state has a set of emitted states, which are observed, each with their own emission probability. In a biological context, states are sequence positions, match states (M) are alignment positions, and emissions are residues. Also applicable to biological sequences are special states for deletion (D) and insertion (I) states, which allow the HMM to be robust to mutations to some extent.

HMMs are built from a multiple sequence alignment - that is, aligned sequences of emitted states. Computers can infer the underlying HMM by interpretation of the observed sequences, and bioinformaticians can use the HMM to classify a novel protein as being part of the group or not. Profile HMMs are built from alignments of related sequences - novel protein sequences which score highly in the HMM indicate homology between the novel protein and the proteins whose sequences built the HMM. Thus, HMMs are useful tools for identifying protein domains in sequence, and are actually used by online bioinformatic tools such as Pfam and SMART, not to mention HMMER itself.

It stands to reason that, if HMMs can identify protein domains, then they can also identify polypeptide and genomic features. Examples of simple HMMs which identify Gln-rich peptide sequences and alternating AT-rich and GC-rich nucleotide sequences are illustrated.

The fact that Pfam, SMART, and HMMER work is evidence enough that HMMs are good enough. However, they are not the holy grail: as is common with Markov models, HMMs have no memory effect. Due to the next state only depending on the current state, long-range secondary and tertiary structure interactions are completely ignored. Only the primary sequence is taken into account. Bioinformaticians turn to co-variance and co-evolutionary methods to study these effects. Second, the model assumes that all amino acid emission probabilities are independent of what has just been emitted, again, due to no memory effect. This is not a good assumption in biology, as, for example, hydrophobic residues usually occur together in the primary sequence so as to clump together and exclude water. Thus, a better model would increase the probability of the occurrence of a hydrophobic residue following a previous hydrophobic, even in the I states.

Further reading:

Eddy, S.R. 1998. "Profile hidden Markov models." Bioinformatics 14 (9):755-763.

Karchin, R. "Hidden Markov Models and Protein Sequence Analysis." [Accessed 2015-11-28.]

Ayraethazide. 2014. "Discrete-time Markov chains." [Accessed 2015-11-28.]

[ernestyalumni]

I'm continuing with my review of HMM Hidden Markov Model with Udacity's Intro to Artificial Intelligence.

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