# Questions tagged [bayesian-statistics]

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### Not grasping Bayesian Monte Carlo

I've read several sources of information that describe the process of Bayesian Monte Carlo Quadrature but am just not understanding the details enough to be able to implement it. For instance two ...
438 views

### How to choose value of additive smoothing in naive Bayes and why a higher value gives bad accuracy?

In Naive Bayes we often do additive smoothing as a fail safe. Consider the following expression: Lets say $$P(X_i) = \frac{count(X_i) + \alpha}{\sum_i^n count(X_i) + \alpha*total\_size}$$ How to tune ...
32 views

### Unsupervised learning: necessity of labels and dependency between features and labels?

I have logs of activities without labels, which describe whether an activity is normal or not. Assuming that normal behaviors will follow a Gaussian distribution, I fit Gaussian distributions on ...
146 views

### Variable elimination in Bayesian network

I'm trying to check if my understanding of variable elimination is correct. Assume the above Bayesian network is factorized as: $p(a,b,d,e,l,s,t,x) = p(a)p(t|a)p(e|t,l)p(x|e)p(l|s)p(b|s)p(d|b,e)p(s)$...
52 views

### Why naive Bayes performs better?

I have found that naive Bayesian classifier performs much better than classification using mixture of multivariate Gaussians. Here is the problem: I have a set of objects with attached features (10 ...
19 views

### Applying Bayes rule in the context of reinforcement learning

I was watching this video on reinforcement learning. At 1:28, it says following: $$Pr(s'|a,z,s)=\frac{Pr(z|s',a,s)Pr(s'|a,s)}{Pr(z|a,s)}$$ I was unable to get how this was obtained. I pondered a bit ...
28 views

### SMO, Random forest and Bayes net algorithms: why does Random forest perform better?

I analyzed a dataset using those 3 different algorithms. As I can see, Random forest performs better in most cases. My dataset is composed of 4000 instances of two classes (class A 2000 elements, ...
49 views

### how many parameters do we need to estimate for a general probabilistic model

Its a question from a test in machine learning. I have 3 binary variables x1,x2 and x3 (which means that each one of them can be either 1 or 0), each one of them has a binary output y (can be 0 or 1). ...
For this question, I have tried everything that I can think of, but cannot solve it. What I want to do is iterate over all possible values of $z_1$, but every method I use, it requires me to know ...