) hypothesis while on the other hand very religious people for example have 0 in their priori when it comes to possibility of their religion being made up so they are forced to ignore evidence to the contrary (because bayesian updating breaks for them due to division by zero and mind's way to signal this exception is denial).In general someone with a lot of weight on given hypothesis is "stubborn" or just very convinced and someone with uniform or close distribution just doesn't know anything about given problem.The course will apply Bayesian methods to several practical problems, to show end-to-end Bayesian analyses that move from framing the question to building models to eliciting prior probabilities to implementing in R (free statistical software) the final posterior distribution.Additionally, the course will introduce credible regions, Bayesian comparisons of means and proportions, Bayesian regression and inference using multiple models, and discussion of Bayesian prediction.Your example is the second one with $\mu_0 = 0$ As a general tip, when doing this type of questions, you should drop the $\frac$, since your expression is only up to a constant of proportionality anyway.(**) You need to expand your expression and write all the exponentials term together, then factorise it as $-\frac$ for some expression $y$. Then you observe, this is proportional the normal distribution with mean and variance given in the wikipedia article.The whole idea is to consider the joint probability of both events, A and B, happening together (a man over 5'10" who plays in the NBA), and then perform some arithmetic on that relationship to provide a updated (posterior) estimate of a prior probability statement.
You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm.For $X_, X_,..., X_$ iid $\mathcal(\theta,\sigma^2)$, and a priori distribution $\theta\sim\mathcal(\mu,\tau^2)$, you should obtain posteriori distribution $\mathcal(\mu_,\tau^2_)$, where: $$\mu_=\frac\quad\text\quad\tau^_=\left(\frac \frac\right)^$$ As for the Bayesian estimator - well, I believe that that would depend on your risk function; with a MSE function, you should obtain $\theta^_=\mu_$.Suppose we observe one draw from the random variable $X$, which is distributed with normal distribution $\mathcal(\mu,\sigma^2)$. (Interpretation: we get noisy signals about $\mu$, which are known to be normally distributed with known variance---this is the draw of $X$.Here is a ten-minute overview of the fundamental idea. But there's a catch: Sometimes the arithmetic can be nasty.
On your way to the hotel you discover that the National Basketball Player's Association is having a convention in town and the official hotel is the one where you are to stay, and furthermore, they have reserved all the rooms but yours.
I have to calculate the posteriori distribution on $\theta$ and the Bayes estimator.