I have a sequential data from time T1 to T6. The rows contain the sequence of states for 50 customers. There are only 3 states in my data. For example, it looks like this: T1 T2 T3 T4 T5 T6 Cust1 C B C A A C My transition matrix X looks like this: A B C A 0.3 0.6 0.1 B 0.5 0.2 0.3 C 0.7 0.1 0.2 Now, we see that at time T6 the state is at …

I'm completely lost when trying to choose the type of predictive model for my problem. Is it autoregressive model, nonlinear time series, Markov Chain or other? Can someone please give me some advise? 78, 18, 51, 89, 19, 43, 62, 28, 94, 49 Suppose, everyday I'm given 10 data, and an example was listed above. They're random numbers generated by two devices, namely Device A and Device B. Each of them is capable to generate random numbers from 0 to …

I could not understand the local independence and global independence of a markov network. Please help me understand with a simple graph

From my understanding you can use the transition matrix to predict the probability of going from the last predicted hidden state(state t), to the t+1 hidden state. My confusion is how in code format do I go from the hidden state predicted at time t+1 to the predicted observed state at time t+1.

This is a captcha where you have to select the side that makes more sense. I am trying to use machine learning trying to break it. My approach is to use Google's vision AI to extract keywords from those images and then use a markov chain and machine learning to predict the correct order. Is there a better approach in your opinion? What are the chances to get it working?

I understand how a Hidden Markov Model is used in genomic sequences, such as finding a gene. But I don't understand how to come up with a particular Markov model. I mean, how many states should the model have? How many possible transitions? Should the model have a loop? How would they know that their model is optimal? Do they imagine, say 10 different models, benchmark those 10 models and publish the best one?

I've looked at hmmlearn but I'm not sure if it's the best one.

If all GAN can do is capture the probability distribution of the dataset, then shouldn't they be similar to handing out images from the dataset? How can we verify that the images that they generate are unique images outside of the dataset?

I downloaded OpenMarkov software for probabilistic graphical models and tried it on mtcars dataset. The mtcars.csv data looks like this: In OpenMarkov GUI, I went to Tools > Learning and loaded mtcars.csv dataset. I then adjusted preprocessing settings to have Discretize with Equal width intervals for all variables. I then chose Hill Climbing algorithm (default) and Automatic learning options. On learning, the result was as follows: My question is what exactly does this figure represent? Does it represent a Bayesian …

How would I train an HMM using the EM algorithm so the transition matrix is upper diagonal?

If I get some posts on reddict.com, how can I predict whether this post will (trending/hot/popular) in the future or not? I would like to use the hidden markov model to predict it, but I don`t know how to define the hidden states and observation sequence...can anyone give me any suggestion? Thanks~

In Neural networks [3.8] : Conditional random fields - Markov network by Hugo Larochelle it seems to me that a Markov Random Field is a special case of a CRF. However, in the Wikipedia article Markov random field it says: One notable variant of a Markov random field is a conditional random field, in which each random variable may also be conditioned upon a set of global observations o. This would mean that CRFs are a special case of MRFs. …