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7

In my view, MCMC/bootstrapping/permutation methods all fall under the category of computational techniques. They aren't tied down to a specific approach or way of thinking about a problem but rather an algorithmic approach to a class of problems. Techniques that involve resampling and iteration don't arise from a machine learning framework, they come out of ...


3

Personally, I find it very hard to draw a line between the two, as there is clearly some overlapping. Machine Learning is a field that is based on classical statistics and USES statistic models heavily. Also, the mathematics behind Machine Learning can get extremely complicated, so I really would not use the mathematical argument as a discriminant. One ...


3

MCMC relies on building a Markov chain whose stationary distribution is a joint distribution you wish to sample from. But you don't start at the stationary distribution, you start at some initial value (in multivariate space). It may take some time for the process to "wash out" the initial conditions. Under suitable conditions the approach to the ...


3

Short answer: this is a code error. If I use the log scale, should I also use a log-proposal? The probability of acceptance is always $$\min\left\{1,\dfrac{\pi(x^\text{new})}{\pi(x^\text{old})}\times\dfrac{q(x^\text{old}|x^\text{new})}{q(x^\text{new}|x^\text{old})} \right\}$$ which can also be written as $$\min\left\{1,\exp[\ln\pi(x^\text{new})-\ln\...


2

You seem to be confusing some things, so let me clarify. First of all, Bayesian models do not have any special connection to time-series. Same as for non-Bayesian models, using Bayesian framework you can define possibly infinite number of different statistical models. Sure, you can first estimate the model for $n$ time-series points, and then update it for ...


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Just for the sake of argument, I am putting my two cents here. As I find the answers above/below so far are pretty explanatory. David DN rounded your question up nicely, I think. This subject is very new and therefore, take what you get and run with it. I worked with stats and I worked in research. I also worked on predictive research. Even the big ...


1

Simulated annealing and Gibbs sampling share the same tool of using a Markov chain to explore the surface of a target function, $f$, but the former aims at finding the global maximum while the latter intends to visit the entire surface in proportion to the altitude. Simulated annealing should thus converge to a single point (assuming there is a single ...


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