45.0090.00

MATCH TIB

Ib qhov yuam kev ntau yam kev ua raws li rau qhov yuav muaj tseeb txij 1.5 mus txog 3.8

Ntshiab
SKU: TSIS TXUAM NROG Qeb:

Hauj lwm lawm

Txoj hau kev yuav tib Match football

Txoj hau kev yuav tib Match football ,hauv cov zauv statistics, twv ua ntej yog ib feem ntawm inference tawm. Peb muab rau koj ncaws pob los kwvyees kom yuav muaj tseeb los mus nrog rau 3.00 mob siab npaum li cas ntawm efficiency. Cov kev tej inference xyov yog hu ua predictive inference, tab sis qhov twv ua ntej yuav tau undertaken li ib yam ntawm cov le caag ntau inference tawm. Tseeb, ib zaum hauj lwm txheeb cais yog tias nws muaj ib tug txhais tau tias cov Identify paub txog tus qauv ntawm ib tug neeg nyob rau cov neeg nyob tag nrho, thiab sib txheeb ceev, Nws yog ib qho tsis tas ib yam li twv ua ntej thaum lub sij hawm. Peb lub tswv yim kom yog thawj koom ruam pab neeg ntawm, cos kws yog cov muaj tus tipsters zoo rau lub lag luam. Thaum cov ntaub ntawv yog kis thoob lub sij hawm, feem ntau rau cov ntsiab lus hais rau lub sij hawm, qhov no hu ua forecasting.Forecasting mas yuav tsum tau sij hawm series txoj kev, Thaum koj twv ua ntej ntau yam ntawm cov ntaub ntawv cross-sectional.

Tshwm heev rua kev siv twv ua ntej xam regression tsom xam thiab nws ntau ncua pawg xws li regression tawm, ua qauv generalized tawm (logistic regression, Poisson regression, Probity regression), thiab lwm yam. Thaum forecasting, Nws pib regressive mus nruab nrab ua qauv thiab yuav muab ntaub vector nws pib regression qauv. Thaum no thiab/lossis lwm yam, generalized txheej regression los yog tshuab kev kawm kev yog deployed hauv pab coj mus muag, teb yog hu ua predictive analytics.

Nyob rau hauv daim ntaub ntawv ntau, xws li lub sij hawm series tsom, Nws tseem tau los laij rau tus qauv uas ua kom muaj lub tswvyim. Yog hais tias ua qauv yuav tsum expressed raws li nws qhov kev siv mus los ntawd tej chaw lav tsis ces smoothed, cov ntaub ntawv uas muaj thiab predicted kwv tau ua Payment.Yog tus generating lwm qauv tawm ces kawg-variance Kalman lim ib yam tsawg kawg-variance smoother yuav siv thiab los rov qab tau cov ntaub ntawv ntawd los ntawm kev ua pa nrov ntsuas. Kev no khi one-step-ntej predictors (minimise lub variance ntawm qhov yuam kev twv ua ntej uas). Thaum tus qauv generating nonlinear ces stepwise linearizations yuav muaj ntaub ntawv nyob rau hauv ncua lim Kalman thiab smoother recursions. Txawm li cas los, Thaum nonlinear, optimum kev kawm ntawv yam tsawg kawg-variance guarantees lawm thov.

Thov koj ua raws li thiab nyiam peb:

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YUAV MUAJ TSEEB

1.5+, 1.9+, 2.2+, 3+