2k 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 ...
500 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 ...
82k 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 ...
770 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 ...
1k 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 ...
4k 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?
2k views

### Bayesian posterior: is multiplying likelihood by prior (rather than simulation) an acceptable approach?

Ken Rice has a helpful introductory set of slides available online called 'Bayesian Statistics (a very brief introduction)'. http://faculty.washington.edu/kenrice/BayesIntroClassEpi515kmr2016.pdf On ...
801 views

### Confusion in Gibbs sampling

I am self-studying Gibbs sampling from a book. The book introduces metropolis hastings algortihm to generate representative values from a posterior distribution. So we know $p(D | \theta) p(\theta)$ ...
272 views

### Bayes in English

I am not a statistician or mathematician but am trying to learn. My question: In Bayes Theorem, $p(C|X)=p(X|C)p(C)/p(X)$, what are the English terms for $p(X|C)$ and $p(C)/p(X)$? In other words, is ...