Iyo yakasimba sisitimu yeBayesian statistics yave kushandiswa zvakanyanya muzvidzidzo zvakawanda, kusanganisira kudzidza muchina.
Bayesian statistics inopa inoshanduka uye inogoneka nzira yekufungidzira, mukupesana neiyo classical statistics, zvinoenderana neyakaiswa paramita uye fungidziro yepfungwa.
Inotigonesa kurangarira ruzivo rwuripo uye kugadzirisa maonero edu kana ruzivo rutsva rwabuda pachena.
Nhamba dzeBayesian dzinotipa kugona kuita kutongwa kwakawanda uye kutora mhedziso dzakavimbika nekugamuchira kusavimbika uye kushandisa mukana wekugovera.
Nzira dzeBayesian dzinopa maonero akasiyana ekuenzanisa hukama hwakaoma, kubata data shoma, uye kubata nekuwedzeredza mumamiriro ezvinhu. machine learning.
Tichatarisa kushanda kwemukati kweBayesian statistics mune ino chinyorwa, pamwe nekushandiswa kwayo uye mabhenefiti mumunda wekudzidza muchina.
Mamwe mazano akakosha muBayesian manhamba anowanzo shandiswa muMachina Kudzidza. Ngationgororei yekutanga; Monte Carlo Method.
Monte Carlo Nzira
MuBayesian statistics, Monte Carlo matekiniki akakosha, uye ane zvakakosha pakushandisa muchina kudzidza.
Monte Carlo inosanganisira kugadzira sampuli dzisina kurongeka kubva pakugovaniswa kusvika kuhuwandu hwakaoma sekuverengera kana kugovera kwemashure.
Iyo Monte Carlo Method inopa nzira inoshanda yekufungidzira huwandu hwechido uye kuongorora yakakwirira-dimensional paramita nzvimbo nekudzokorora sampling kubva pakugovera kufarira uye avhareji yezviwanikwa.
Zvichienderana nenhamba dzekuenzanisa, nzira iyi inobatsira vaongorori kuita sarudzo ine ruzivo, kuyera kusavimbika, uye kuwana zviwanikwa zvakasimba.
Kushandisa Monte Carlo Kuverengera Kunobudirira
Kuverengera kugoverwa kwemashure muBayesian statistics kazhinji kunoda zvakaomarara zvakabatanidzwa.
Iko kukwenenzverwa kwakanaka kweizvi zvakabatanidzwa zvakapihwa neMonte Carlo tekinoroji inotigonesa kunyatsoongorora kugovera kwemashure.
Izvi zvakakosha mukudzidza kwemuchina, uko mamodheru akaomarara uye akakwira-dimensional paramita nzvimbo zvinowanzoitika.
Nekunyatso kufungidzira zvinosiyana zvekufarira senge tarisiro kukosha, histograms, uye kusarudzika tichishandisa Monte Carlo matekiniki, isu takashongedzerwa zvirinani kuongorora iyo data uye kutora mhedziso kubva kwairi.
Kutora Sample kubva kuPosterior Distribution
MuBayesian inference, sampling kubva kune posterior kugovera inhanho yakakosha.
Iko kugona kuenzanisa kubva kumashure kwakakosha mumashini ekudzidza maapplication, kwatinoyedza kudzidza kubva kune data uye kugadzira fungidziro.
Nzira dzeMonte Carlo dzinopa nzira dzakasiyana siyana dzesampling kubva kugovera zvisina tsarukano, kusanganisira yekumashure.
Aya maitiro, ayo anosanganisira inversion nzira, nzira yekuumbwa, nzira yekuramba, uye kukosha sampling, inotigonesa kutora masampula anomiririra kubva kumashure, zvichitibvumira kuongorora uye kunzwisisa kusava nechokwadi kunoenderana nemamodheru edu.
Monte Carlo muKudzidza kweMichina
Monte Carlo algorithms anowanzo shandiswa mukudzidza kwemuchina kufungidzira kugovera kwemashure, iyo inofukidza kusavimbika kweiyo modhi paramita yakapihwa yakacherechedzwa data.
Maitiro eMonte Carlo anogonesa kuyerwa kwekusava nechokwadi uye fungidziro yehuwandu hwekufarira, senge tarisiro yemitengo uye maitiro emuenzaniso zviratidzo, nesampling kubva kumashure kwekugovera.
Aya masampuli anoshandiswa munzira dzakasiyana dzekudzidza kugadzira fungidziro, kuita sarudzo yemhando, kuyera modhi yakaoma, uye kuita Bayesian inference.
Uyezve, hunyanzvi hweMonte Carlo hunopa humbowo hwakasiyana-siyana hwekubata neakakwira-dimensional paramita nzvimbo uye akaomeswa modhi, achibvumira kukurumidza kwemashure ekugovera kutsvagisa uye kwakasimba kuita sarudzo.
Mukupedzisa, matekiniki eMonte Carlo akakosha mukudzidza kwemuchina nekuti anofambisa kuyerwa kwekusava nechokwadi, kuita sarudzo, uye kufungidzira zvichibva pakugovera kwemashure.
Markov Chains
Markov cheni imhando dzemasvomhu dzinoshandiswa kurondedzera stochastic maitiro umo mamiriro ehurongwa pane imwe nguva inotemwa chete neyakapfuura mamiriro.
A Markov cheni, mumashoko akareruka, kutevedzana kwezviitiko zvisina tsarukano kana nyika umo mukana wekuchinja kubva kune imwe nyika kuenda kune imwe unotsanangurwa neseti yezvinobvira inozivikanwa seshanduko zvingangoitika.
Makov cheni anoshandiswa mufizikisi, hupfumi, uye sainzi yekombuta, uye inopa hwaro hwakasimba hwekudzidza uye kutevedzera masystem akaomarara ane probabilistic maitiro.
Makov maketani akabatana zvakanyanya nekudzidza kwemuchina nekuti anobvumidza iwe kuenzanisira uye kuongorora hukama hwakasiyana uye kugadzira masampuli kubva kwakaoma mukana wekugovera.
Markov cheni dzinoshandiswa mukudzidza muchina kune maapplication akadai sekuwedzera data, kutevedzana modhi, uye generative modelling.
Makine ekudzidza emuchina anogona kutora epasi mapatani uye hukama nekuvaka uye kudzidzisa Markov cheni modhi pane yakacherechedzwa data, zvichiita kuti ive yakakosha kumashandisirwo akadai sekuzivikanwa kwekutaura, kugadzirwa kwemutauro wechisikigo, uye nguva yekuongorora yakatevedzana.
Makov cheni dzakanyanya kukosha muMonte Carlo matekiniki, achibvumira kuenzanirana kwakanaka uye fungidziro yekufungidzira muBayesian muchina kudzidza, iyo ine chinangwa chekufungidzira kuseri kwekugovera kwakapihwa data rakacherechedzwa.
Ikozvino, pane imwe pfungwa yakakosha muBayesian Statistics iri kugadzira nhamba dzisina kurongeka dzekugovera zvisina tsarukano. Ngationei kuti zvinobatsira sei kudzidza muchina.
Random Number Generation for Arbitrary Distributions
Kune akasiyana emabasa mukudzidza kwemuchina, kugona kuburitsa nhamba dzisina kurongeka kubva mukugovera zvisina tsarukano kwakakosha.
Nzira mbiri dzakakurumbira dzekuzadzisa chinangwa ichi ndeye inversion algorithm uye yekubvuma-kuramba algorithm.
Inversion algorithm
Tinogona kuwana manhamba asina kurongeka kubva mukugovera ine inozivikanwa cumulative distribution function (CDF) tichishandisa inversion algorithm.
Isu tinogona kushandura manhamba asina kurongeka kuita manhamba asina kurongeka nekugovera kwakakodzera nekudzoreredza CDF.
Iyi nzira yakafanira kumashini ekudzidza maapplication anodaidzira sampling kubva kune inozivikanwa kugovera sezvo ichishanda uye inowanzo shanda.
Kubvuma-Kuramba Algorithm
Kana yakajairwa algorithm isingawanikwe, iyo yekubvuma-kuramba algorithm inzira inogoneka uye inoshanda yekugadzira nhamba dzisina kurongeka.
Neiyi nzira, nhamba dzisina kurongeka dzinogamuchirwa kana kurambwa zvichienderana nekuenzanisa nebasa rehamvuropu. Inoshanda sekuwedzera kwemaitiro ekuumbwa uye yakakosha pakugadzira masampuli kubva mukugovewa kwakaoma.
Muchidzidzo chemuchina, iyo yekubvuma-kuramba algorithm inonyanya kukosha kana uchibata nemultidimensional nyaya kana mamiriro apo yakatwasuka yekuongorora inversion tekinoroji isingashande.
Kushandiswa MuHupenyu Hwechokwadi uye Zvinetso
Kutsvaga mabasa emvuropu akakodzera kana fungidziro dzinonyanya kugovera chinangwa kunodiwa kuti nzira mbiri dziite.
Izvi zvinowanzoda kunzwisiswa kwakakwana kwezvinhu zvekugovera.
Chimwe chinhu chakakosha chekufunga nezvacho chiyero chekugamuchira, chinoyera kushanda kwegorgorithm.
Nekuda kwekuoma kwekugovera uye kutukwa kwechimiro, nzira yekubvuma-yekuramba inogona, zvisinei, inonetsa munhau dzepamusoro. Dzimwe nzira dzinodiwa kugadzirisa matambudziko aya.
Kuvandudza Kudzidza kweMichina
Kune mabasa akaita sekuwedzera data, kumisa modhi, uye fungidziro yekusavimbika, kudzidza muchina kunoda kugadzirwa kwezviverengeka zvisina tsarukano kubva mukugovera zvisina tsarukano.
Machine yekudzidza algorithms inogona kusarudza masampuli kubva kwakasiyana-siyana kugovera nekushandisa inversion uye yekubvuma-yekuramba nzira, zvichibvumira kuti iwedzere kuchinjika modhi uye nekusimudzira kuita.
MuBayesian muchina kudzidza, uko kugovera kwemashure kunowanzoda kufungidzirwa nesampling, nzira idzi dzinobatsira zvakanyanya.
Zvino, ngatiendererei kune imwe pfungwa.
Nhanganyaya kuABC (Approximate Bayesian Computation)
Approximate Bayesian Computation (ABC) inzira yenhamba inoshandiswa pakuverenga mukana wekuita, iyo inotaridza mukana wekupupurira data rakapihwa modhi paramita, inonetsa.
Panzvimbo yekuverenga iyo ingangoita basa, ABC inoshandisa simulations kugadzira data kubva kune modhi ine mamwe maitiro eparameter.
Iyo yakateedzerwa uye yakacherechedzwa data inozoenzaniswa, uye parameter marongero anogadzira anofananidzwa masimulation anochengetwa.
Kufungidzira kwakakasharara kwekugoverwa kwemashure kweiyo paramita kunogona kugadzirwa nekudzokorora iyi nzira nenhamba yakawanda yekufananidza, ichibvumira kufungidzira kweBayesian.
Iyo ABC Concept
Iyo yakakosha pfungwa yeABC ndeyekuenzanisa data rakateedzerwa rakagadzirwa nemuenzaniso kuti richerechedze data pasina kujekesa komputa iyo ingangoita basa.
ABC inoshanda nekumisikidza chinhambwe kana kusaenzana metric pakati pekucherechedzwa uye kuyedzerwa data.
Kana chinhambwe chiri pasi pechimwe chikumbaridzo, iyo parameter tsika dzinoshandiswa kugadzira inosanganisirwa inofananidzira inofungidzirwa kuva inonzwisisika.
ABC inogadzira fungidziro yekugovera kwemashure nekudzokorora iyi nzira yekubvuma-kurambwa nemhando dzakasiyana dzeparameter, kuratidza inonzwisisika parameter tsika dzakapihwa data rakacherechedzwa.
Machine Learning's ABCs
ABC inoshandiswa mukudzidza muchina, kunyanya kana mukana-wakavakirwa kufungidzira wakaoma nekuda kwemhando dzakaoma kana computationally kudhura. ABC inogona kushandiswa kune akasiyana maapplication anosanganisira kusarudza modhi, fungidziro yeparameter, uye generative modelling.
ABC mukudzidza kwemuchina inoita kuti vaongorori vatore zvinongedzo nezve modhi paramita uye vasarudze mhando dzemhando yepamusoro nekuenzanisa data rakateedzerwa uye chairo.
Machine yekudzidza algorithms inokwanisa kuwana ruzivo rwekusavimbika kwemuenzaniso, kuita kuenzanisa kwemuenzaniso, uye kugadzira fungidziro zvichibva pane zvakacherechedzwa data nekuyeresa kugoverwa kwemashure kuburikidza neABC, kunyangwe kana mukana wekuongorora uchidhura kana usingaite.
mhedziso
Chekupedzisira, nhamba dzeBayesian dzinopa hwaro hwakasimba hwekufungidzira uye kuenzanisira mukudzidza kwemichina, zvichitibvumira kubatanidza ruzivo rwekare, kubata nekusagadzikana, uye kuwana mhedzisiro yakavimbika.
Nzira dzeMonte Carlo dzakakosha muhuwandu hweBayesian uye kudzidza kwemuchina nekuti dzinobvumira kuongororwa kwakanaka kwenzvimbo dzakaomesesa paramende, fungidziro yehunhu hwekufarira, uye sampling kubva kumashure kwekugovera.
Makov cheni anowedzera kugona kwedu kutsanangura uye kutevedzera probabilistic masisitimu, uye kugadzira nhamba dzisina kurongeka dzekugovera kwakasiyana kunobvumira kuchinjika modhi uye kuita zvirinani.
Chekupedzisira, Approximate Bayesian Computation (ABC) inzira inobatsira yekuita yakaoma mukana computations uye kugadzira mitongo yeBayesian mukudzidza kwemichina.
Tinogona kukudziridza kunzwisisa kwedu, kuvandudza mamodheru, uye kuita kutonga kwakadzidziswa mumunda wekudzidza muchina nekushandisa misimboti iyi.
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