# Tag Info

No, you can't extract any probability from a fuzzy membership grade. The uncertainty expressed by fuzzy logic is about partial truth, not about probability. $\mu_S(x) = 0.9$ doesn't mean that "$x$ is tall" is true with a probability of 0.9, but that "$x$ is tall" is 90% true (notice the difference in semantics). You have to think ...
Also, in general, in the conditional expectation, which distribution do we compute the expectation with respect to? From what I have seen, in $\mathbb{E}[X|Y]$, we always calculate the expected value over distribution $X$. No, for $\mathbb{E}[X|Y]$ we take expectation of $X$ with respect to the conditional distribution $X|Y$, i.e. $$\mathbb{E}[X|Y] = \... 3 Bayes Error Rate For the general case of K different classes, the probability of classifing x instance correctly is: $$\label{eq1} \begin{split} P(correct) & = \sum_{i=1}^{K} p(x \in H_i, C_i) \\ & = \sum_{i=1}^{K} \int_{x \in H_i} p(x,C_i) \, dx\\ & = \sum_{i=1}^{K} \int_{x \in H_i} P(C_i|x)p(x)\,dx \\ \end{split}$$ ... 2 Of course you can use AI (specially Deep Learning) in your application. your covariates will be the input to your AI model and the model should predict probability of presence. The model has no problem with binary data and binary data is common in this field. Also note that 1:100 ratio is not good and the network will probably learn to output absence for any ... 2 Although this question is slightly primarily opinion-based and too broad (and I will probably close it as such) and a good answer will necessarily depend on your background, I will list some of the main theoretical prerequisites that everyone should ideally be familiar with before diving into TensorFlow Probability (TFP). I am familiar with TFP, given that ... 1 Figure 3 in the original WGAN paper is actually quite helpful to understand the difference between the score in WGAN and the probability in GAN (see screenshot below). The blue distribution are real samples, and the green one are fake samples. The Vanilla GAN trained in this example identifies the real samples as '100% real' (red curve) and the fake samples ... 1 Let A and B be two events. In general, the probability that either A or B occurs is defined as$$ P(A \text{ or } B) = P(A) + P(B) - P(A \text{ and } B) $$If A and B are disjoint, i.e. they cannot happen at the same time, then P(A \text{ and } B) = 0, so the above formula becomes$$ P(A \text{ or } B) = P(A) + P(B)  If the probability of ...