5k views

Why ignore the denominator of bayes rule? [duplicate]

I am a new beginner in stats. I have specifically diverted my attention towards this because, I wish to understand the concept of Deep Bayesian Learning, so I am starting with the basics. The question ...
2k views

Why is the normalization necesary in Bayesian inference? [duplicate]

In this post it reads that: normalization can be intractable when applying Bayes’ Theorem And in this answer it says that: it does not depend on the parameters since these have been ...
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762 views

Is the density of the unnormalised posterior distribution the same as the density for normalised posterior? [duplicate]

The posterior distribution is proportional to the likelihood times the prior distribution $p(\theta|D) \propto p(D|\theta)p(\theta)$. Computing $p(D|\theta)p(\theta)$ would give the un-normalised ...
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422 views

How is the "proportional" calculated in Bayes theorem? [duplicate]

I am struggling with the Wikipedia entry on Likelihood. In an example it mentions $L(P_H = 0.5 |HH) = 0.25$ It mentions that Bayes' theorem implies that the posterior probability is proportional to ...
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126k views

Can a probability distribution value exceeding 1 be OK?

On the Wikipedia page about naive Bayes classifiers, there is this line: $p(\mathrm{height}|\mathrm{male}) = 1.5789$ (A probability distribution over 1 is OK. It is the area under the bell curve ...
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2k views

What is the "grid" in Bayesian grid approximations?

I tried looking for my specific question, but I only found partially related questions here, here, and here. I think my question is much simpler than what was asked and answered in these queries. I'm ...
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3k views

What does it mean intuitively to know a pdf "up to a constant"?

I've seen this mentioned numerous times, most recently in motivating the MCMC method and description of the Metropolis-Hastings algorithm. The text (Simulation and the Monte Carlo Method, Second ...
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3k views

Intuition of Bayesian normalizing constant

In the commonly mentioned mammography screening problem with a screening likelihood of 80%, a prior of 10% and a false positive rate of 50%, or its variants, it is easy to explain that the conditional ...
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10k views

With the Naive Bayes classifier, why do we have to normalize the probabilities after calculating the probabilities of each hypothesis?

In the Naive Bayes classifier, why do we have to normalize the probabilities after calculating the probabilities of each hypothesis?
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