Editing Hidden Markov model (section)

== Applications ==
[[File:A profile HMM modelling a multiple sequence alignment.png|thumb|right|A profile HMM modelling a multiple sequence alignment of proteins in [[Pfam]]]]

HMMs can be applied in many fields where the goal is to recover a data sequence that is not immediately observable (but other data that depend on the sequence are). Applications include:
* [[Computational finance]]<ref>{{cite journal |doi=10.1007/s10614-016-9579-y |volume=49 |issue=4 |title=Parallel Optimization of Sparse Portfolios with AR-HMMs |year=2016 |journal=Computational Economics |pages=563–578 |last1=Sipos |first1=I. Róbert |last2=Ceffer |first2=Attila |last3=Levendovszky |first3=János|s2cid=61882456}}</ref><ref>{{cite journal |doi=10.1016/j.eswa.2016.01.015 |volume=53 |title=A novel corporate credit rating system based on Student's-t hidden Markov models |year=2016 |journal=Expert Systems with Applications |pages=87–105 |last1=Petropoulos |first1=Anastasios |last2=Chatzis |first2=Sotirios P. |last3=Xanthopoulos |first3=Stylianos}}</ref>
* [[Single-molecule experiment|Single-molecule kinetic analysis]]<ref>{{cite journal |last1=Nicolai |first1=Christopher |date=2013 |doi=10.1142/S1793048013300053 |title=Solving Ion Channel Kinetics with the QuB Software |journal=Biophysical Reviews and Letters |volume=8 |issue=3n04 |pages=191–211}}</ref>
* [[Neuroscience]]<ref>{{cite journal |doi=10.1002/hbm.25835 |title=Spatiotemporally Resolved Multivariate Pattern Analysis for M/EEG |journal=Human Brain Mapping |date=2022 |last1=Higgins |first1=Cameron |last2=Vidaurre |first2=Diego |last3=Kolling |first3=Nils |last4=Liu |first4=Yunzhe |last5=Behrens |first5=Tim |last6=Woolrich |first6=Mark |volume=43 |issue=10 |pages=3062–3085 |pmid=35302683 |pmc=9188977}}</ref><ref>{{Cite journal |last1=Diomedi |first1=S. |last2=Vaccari |first2=F. E. |last3=Galletti |first3=C. |last4=Hadjidimitrakis |first4=K. |last5=Fattori |first5=P. |date=2021-10-01 |title=Motor-like neural dynamics in two parietal areas during arm reaching |url=https://www.sciencedirect.com/science/article/pii/S0301008221001301 |journal=Progress in Neurobiology |language=en |volume=205 |pages=102116 |doi=10.1016/j.pneurobio.2021.102116 |pmid=34217822 |issn=0301-0082|hdl=11585/834094 |s2cid=235703641 |hdl-access=free}}</ref>
* [[Cryptanalysis]]
* [[Speech recognition]], including [[Siri]]<ref>{{cite book|last1=Domingos|first1=Pedro|title=The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World|url=https://archive.org/details/masteralgorithmh0000domi|url-access=registration|date=2015|publisher=Basic Books|isbn=9780465061921|page=[https://archive.org/details/masteralgorithmh0000domi/page/37 37]|language=en}}</ref>
* [[Speech synthesis]]
* [[Part-of-speech tagging]]
* Document separation in scanning solutions
* [[Machine translation]]
* [[Partial discharge]]
* [[Gene prediction]]
* [[Handwriting recognition]]<ref>Kundu, Amlan, Yang He, and Paramvir Bahl. "[https://www.academia.edu/download/48589949/0031-3203_2889_2990076-920160905-24541-r9o2lm.pdf Recognition of handwritten word: first and second order hidden Markov model based approach]{{dead link|date=July 2022|bot=medic}}{{cbignore|bot=medic}}." Pattern recognition 22.3 (1989): 283-297.</ref>
* [[sequence alignment|Alignment of bio-sequences]]
* [[Time series|Time series analysis]]
* [[Activity recognition]]
* [[Protein folding]]<ref>{{Cite journal |last1=Stigler |first1=J. |last2=Ziegler |first2=F. |last3=Gieseke |first3=A. |last4=Gebhardt |first4=J. C. M. |last5=Rief |first5=M. |title=The Complex Folding Network of Single Calmodulin Molecules |doi=10.1126/science.1207598 |journal=[[Science (journal)|Science]] |volume=334 |issue=6055 |pages=512–516 |year=2011 |pmid=22034433 |bibcode=2011Sci...334..512S |s2cid=5502662}}</ref>
* Sequence classification<ref>{{Cite journal |last1=Blasiak |first1=S. |last2=Rangwala |first2=H. |title=A Hidden Markov Model Variant for Sequence Classification |journal=IJCAI Proceedings-International Joint Conference on Artificial Intelligence |volume=22 |pages=1192 |year=2011}}</ref>
* [[Metamorphic virus detection]]<ref>{{Cite journal |last1=Wong |first1=W. |last2=Stamp |first2=M. |doi=10.1007/s11416-006-0028-7 |title=Hunting for metamorphic engines |journal=Journal in Computer Virology |volume=2 |issue=3 |pages=211–229 |year=2006 |s2cid=8116065}}</ref>
* [[Sequence motif]] discovery (DNA and proteins)<ref>{{Cite journal |last1=Wong |first1=K. -C. |last2=Chan |first2=T. -M. |last3=Peng |first3=C. |last4=Li |first4=Y. |last5=Zhang |first5=Z. |title=DNA motif elucidation using belief propagation |doi=10.1093/nar/gkt574 |journal=Nucleic Acids Research |volume=41 |issue=16 |pages=e153 |year=2013 |pmid=23814189 |pmc=3763557}}</ref>
* DNA hybridization kinetics<ref>{{Cite journal|last1=Shah|first1=Shalin|last2=Dubey|first2=Abhishek K.|last3=Reif|first3=John|date=2019-05-17|title=Improved Optical Multiplexing with Temporal DNA Barcodes|journal=ACS Synthetic Biology|volume=8|issue=5|pages=1100–1111|doi=10.1021/acssynbio.9b00010|pmid=30951289|s2cid=96448257}}</ref><ref>{{Cite journal|last1=Shah|first1=Shalin|last2=Dubey|first2=Abhishek K.|last3=Reif|first3=John|date=2019-04-10|title=Programming Temporal DNA Barcodes for Single-Molecule Fingerprinting|journal=Nano Letters|volume=19|issue=4|pages=2668–2673|doi=10.1021/acs.nanolett.9b00590|pmid=30896178|bibcode=2019NanoL..19.2668S|s2cid=84841635|issn=1530-6984}}</ref>
*[[Chromatin]] state discovery<ref>{{Cite web|url=http://compbio.mit.edu/ChromHMM/|title=ChromHMM: Chromatin state discovery and characterization|website=compbio.mit.edu|access-date=2018-08-01}}</ref>
*[[Transportation forecasting]]<ref>{{Cite arXiv|title=Modeling and Forecasting the Evolution of Preferences over Time: A Hidden Markov Model of Travel Behavior|last=El Zarwi|first=Feraz|date=May 2011|eprint = 1707.09133|class = stat.AP}}</ref>
*[[Solar irradiance]] variability<ref>{{Cite journal|title=The stochastic two-state solar irradiance model (STSIM)|last1=Morf|first1=H.|journal=Solar Energy|volume=62|issue=2|pages=101–112|date=Feb 1998|bibcode=1998SoEn...62..101M|doi=10.1016/S0038-092X(98)00004-8}}</ref><ref>{{Cite journal|title=A Markov-chain probability distribution mixture approach to the clear-sky index|last1=Munkhammar|first1=J.|last2=Widén|first2=J.|journal = Solar Energy|date=Aug 2018|volume=170|pages=174–183|bibcode=2018SoEn..170..174M|doi=10.1016/j.solener.2018.05.055|s2cid=125867684}}</ref><ref>{{Cite journal|title=An N-state Markov-chain mixture distribution model of the clear-sky index|last1=Munkhammar|first1=J.|last2=Widén|first2=J.|journal=Solar Energy|volume=173|pages=487–495|date=Oct 2018|bibcode=2018SoEn..173..487M|doi=10.1016/j.solener.2018.07.056|s2cid=125538244}}</ref>