CU-5t5y0p Adds to glossary prior, posterior and related definitions

[olgadk7]

Adds Prior, Posterior (+ Likelihood, Bayesian model, and Bayesian data analysis) definitions to docs/source/glossary.md

[OriolAbril]

Same general comment as in #4984 (review), I haven't had time to go over the definitions in detail yet

[itsguneetsingh]

For some reason I am not able to make a PR to patch1 branch of @olgadk7 repo. So I suppose I should just make a suggestion for the definitions here. Is that ok @michaelosthege ?

[olgadk7]

Will be adding sources for the definitions in the next couple of days finally, thank you.

[martinacantaro]

Task linked: CU-5t5y0p Glossary

[martinacantaro]

For some reason I am not able to make a PR to patch1 branch of @olgadk7 repo. So I suppose I should just make a suggestion for the definitions here. Is that ok @michaelosthege ?

HI @Guneetconvent2002 ! Let me know if you need anything!

[OriolAbril]

I think the definitions look great already, and we can always continue improving them iteratively.

I see two options now. @olgadk7 if you have time today or tomorrow, it would be great to add the :sorted: thing right below the start of the glossary directive, if you have time also add some extra cross-references between terms (i.e. the prior definition references he likelihood and the posterior and could be linked, things like that). Otherwise I think we should merge now and take care of sorting in a follow-up PR

[olgadk7]

@OriolAbril yep done, thank you.

[itsguneetsingh]

I couldn't see the suggest changes button so I've made a review for adding some more definitions. I think that these definitions are fine so if someone could have a look at this and @olgadk7 if you could then add these to the file

[itsguneetsingh]

Equidispersion
If in a Poisson distribution if the variance equals the mean of the distribution, it is reffered to as equidispersion.

Generalized Poisson PMF
A generalization of the {term}Poisson distribution, with two parameters X1, and X2, is obtained as a limiting form of the {term}generalized negative binomial distribution. The variance of the distribution is greater than, equal to or smaller than the mean according as X2 is positive, zero or negative. For formula and more detail, visit the link in the title.

Bayes' theorem
Describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if the risk of developing health problems is known to increase with age, Bayes' theorem allows the risk to an individual of a known age to be assessed more accurately (by conditioning it on their age) than simply assuming that the individual is typical of the population as a whole.

Formula:
$$
P(A|B) = \frac{P(B|A) P(A}{Pr(B)}
$$
Where A and B are events and P(B) != 0

Markov Chain
A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event.

Markov Chain Monte Carlo
[MCMC]
Markov chain Monte Carlo (MCMC) methods comprise a class of algorithms for sampling from a probability distribution. By constructing a {term}Markov Chain that has the desired distribution as its equilibrium distribution, one can obtain a sample of the desired distribution by recording states from the chain. Various algorithms exist for constructing chains, including the Metropolis–Hastings algorithm.