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Results tagged with bayesian
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user 105137
Bayesian inference is a method of statistical inference that relies on treating the model parameters as random variables and applying Bayes' theorem to deduce subjective probability statements about the parameters or hypotheses, conditional on the observed dataset.
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What is the likelihood function of having heads 8 times out of 10 toss
Then the Bayesian update will be:
likelihood = []
for i in range(11):
likelihood.append(binom.pmf(n=10,k=i,p=0.8))
# first is prior, second is posterior
second = np.multiply(first, likelihood)
second … Introduction to Bayesian statistics, part 1: The basic concepts says that Bayesian regression posterior is prior x likelihood and normalisation, and the likelihood function for coin toss is binominal(10 …
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What is the real-life benefit and application of Bayesian regression [closed]
Question
What is the real-life example of the benefit and application of the benefit of Bayesian regression? … What's the advantages of bayesian version of linear regression, logistic regression etc (1)
compare bayesian linear regression VS linear regression [closed] (2)
第14回 ベイズ線形回帰を実装してみよう (implement bayesian …
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MCMC - how to compute prior(𝜃)
From Scratch: Bayesian Inference, Markov Chain Monte Carlo and Metropolis Hastings, in python
is using 1. …
- 1,568
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How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
We want to know the posterior distribution $P(\theta)$ and where modes are, this is the goal.
But we cannot calculate $P(\theta)$ analytically, this is the problem.
However, we can build a Markov Cha …
- 1,568
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What exactly does it mean to and why must one update prior?
Because I think we want a model that incorporated the data observed so that the model (probability distribution) fits the data observed and we can use the model for stable predictions, as the initial …
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