Tasuta kohaletoimetamine tellimustele üle 29 €
Olibro

Minu ostukorv

Laadimine...

Kliendi sisselogimine

Uue kliendina kassasse

Konto loomisel on palju eeliseid:

  • Vaata tellimuse ja saatmise staatust
  • Jälgi tellimuste ajalugu
  • Kiirem väljakontroll
Loo konto
  • check 10+ miljonit raamatut
  • check Uued tooted iga päev
  • check Meid usaldab üle 1 miljoni kliendi
  • check Hea hind ja allahindlused
  • check Tarne üle kogu Euroopa

Bayesi järeldus

Bayes'i järeldus on statistika ja tõenäosusteooria oluline osa, mis pakub selle aluseks olevaid matemaatilisi meetodeid, et värskeid andmeid varasemate teadmistega kombineerida. Selle kategooria raamatud katab laia valikut teemasid, alates praktilistest rakendustest kuni teoreetiliste aruteludeni, suunatud nii üliõpilastele kui ka professionaalidele, kes soovivad süvitsi minna andmete analüüsi ja tõendusmaterjali tõlgendamise maailma.

Esemed 1-30 158-st

Kategooria "Bayesi järeldus"

Bayesian inference represents a powerful statistical framework that allows for the incorporation of prior knowledge along with new evidence. This methodology is particularly beneficial for those in fields such as data science, economics, and the social sciences, where making informed decisions based on limited data is crucial. Its roots can be traced back to the work of Thomas Bayes in the 18th century, and over the years, it has evolved into a key tool for statisticians and researchers alike.

One of the primary advantages of Bayesian inference is its ability to provide a probabilistic interpretation of uncertainty, enabling more nuanced conclusions than traditional methods. Readers interested in this category will discover a wealth of resources that explore both the theoretical foundations and practical applications of Bayesian methods. Whether one is a student delving into the complexities of probability or a seasoned statistician seeking to refine their analytical skills, the literature available here caters to a range of expertise levels.

This category encapsulates a broad spectrum of topics related to Bayesian inference, inviting readers to explore various aspects such as model selection, hypothesis testing, and data analysis techniques. Each book serves as a stepping stone in enhancing one’s comprehension of how Bayesian statistics can be applied to real-world scenarios.

Authors in this category often include renowned statisticians and thought leaders who have contributed to the field of Bayesian analysis. Their insights not only illuminate the subject matter but also inspire readers to adopt these modern statistical approaches in their own work. The combination of rich historical context, theoretical exploration, and practical application makes this collection invaluable for anyone keen on mastering the art of Bayesian inference.