You can choose to create a Unity ID or use one of the social sign-ins. If you do not have an account, follow the prompts to create one. If you already have an account, sign in, choose your licenses type, and proceed to the Installing the Unity Editor section. To install and use the Unity Editor, you must have a Unity Developer Network (UDN) account. Go to the directory where UnityHub.AppImage is.Note: If Unity Hub fails to launch while you are using Linux, you might need to give UnityHub.AppImage executable permissions. Unity officially supports the following Linux distributions: To install the Unity Hub for Windows, macOS, and Linux visit Download Unity on the Unity website. Use the Hub to manage multiple installations of the Unity Editor along with their associated components, create new Projects, and open existing Projects. Let's make this discussion concrete by returning to our binomial example.The Unity Hub is a management tool that you can use to manage all of your Unity Projects and installations. That's because, again, way back in Stat 414, we showed that if \(Z\)is a random variable, then the expected value of the absolute value of the error, that is, \(E(|Z-b|]\) is minimized when \(b\) equals the median of the distribution. Then in order to make her cost be as small as possible, she'll want to make her guess \(w(y)\) be the conditional median. On the other hand, if she is charged the absolute value of the error between \(\theta\) and her guess \(w(y)\), that is: That's because way back in Stat 414, we showed that if \(Z\) is a random variable, then the expected value of the squared error, that is, \(E\left\) is minimized at \(b=E(Z)\). there we have it! Because she wants her cost to be as small as possible, she should make her guess \(w(y)\) be the conditional mean \(E(\theta|y)\). Suppose, she is charged the square of the error between \(\theta\) and her guess \(w(y)\). Well, this Bayesian woman would probably want the cost of her error to be as small as possible. Well, let's suppose she gets charged a certain amount for estimating the real value of the parameter \(\theta\) with her guess \(w(y)\). Huh? Cost? We're talking about estimating a parameter not buying groceries. should she calculate the mean or the median? Well, that depends on what it will cost her for using either. errrr, let's get rid of this he or she stuff. But how? Well, the logical thing to do would be to use \(k(\theta|y)\) to calculate the mean or median of \(\theta\), as they would all be reasonable guesses of the value of \(\theta\). So, if a Bayesian is asked to make a point estimate of \(\theta\), he or she is going to naturally turn to \(k(\theta|y)\) for the answer. Okay now, are you scratching your head wondering what this all has to do with Bayesian estimation, as the title of this page suggests it should? Well, let's talk about that then! Bayesians believe that everything you need to know about a parameter \(\theta\) can be found in its posterior p.d.f. with parameters \(y+\alpha\) and \(n-y+\beta\). \(P(\lambda=3 | X=7) = \dfrac\)įor \(0<\theta<1\), which you might recognize as a beta p.d.f. Now, simply by using the definition of conditional probability, we know that the probability that \(\lambda=3\) given that \(X=7\) is: Just stick your hand in your probability tool box, and pull out Bayes' Theorem. In one fell swoop, we've just turned everything that we've learned in Stat 414 and Stat 415 on its head! The next thing you should notice, after recovering from the dizziness of your headstand, is that we already have the tools necessary to calculate the desired probabilities. The first thing you should notice in this example is that we are talking about finding the probability that a parameter \(\lambda\) takes on a particular value.
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