hidden-markov-models

I have a continuous univariate xts object of length 1000, which I have converted into a data.frame called x to be used by the package RHmm . I have already chosen that there are going to be 5 states and 4 gaussian distributions in the mixed distribution. What I'm after is the expected mean value for the next observation. How do I go about getting that? So what I have so far is: a transition matrix from running the HMMFit() function a set of means and variances for each of the gaussian distributions in the mixture, along with their respective proportions, all of which was also generated form

Generic formulation I have a dataset consisting of a sequence of points with 12 features each. I am interested in detecting an event in this data. In the training data I know the moments the event occurred. When the event occurs I can see an observable pattern in the sequence of points before the event. The pattern is formed from about 300 consecutive points. I am interested in detecting when the event occurred in a infinite sequence of points. The analysis happens post factum. I am not interested in predicting if the event will occur. Concrete example You may skip this section I am building a

A Simple Markow Chain Let's say we want to estimate parameters of a system such that we can predict the state of the system at timestep t+1 given the state at timestep t. PyMC should be able to deal with this easily. Let our toy system consist of a moving object in a 1D world. The state is the position of the object. We want to estimate the latent variable/the speed of the object. The next state depends on the previous state and the latent variable the speed. # define the system and the data true_vel = .2 true_pos = 0 true_positions = [.2 * step for step in range(100)] We assume that we have

I am trying to classify 9 types of crops (Y) from 5 satellite images (X), acquired at dates t = {t1,...,t5}, corresponding to 5 crop phrenology states (S) i.e. preparation, seeding, growing, harvesting and post-harvesting stages. I am using similar training parcels throughout therefore the prior probabilities (initial probabilities) are equal. I have computed the likelihood model (Emission Probabilities) P(X|Y) from each image using Gaussian mixture model and I have state Transition probabilities for each crop. My question is how do I integrate this into HMM using this library to determine the

Consider input matrices X and Y of shapes (m,m) and (n,n) respectively. As an output we need to give a (mn,mn) shape matrix such that it multiplies corresponding entries in the two matrices. These two matrices X and Y represent transition matrices. A following example can be taken to illustrate the required output. Here, X is a 3*3 matrix and Y is a 2*2 matrix. Matrix X -------------- x1 x2 x3 x1| a b c x2| d e f x3| g h i Matrix Y -------------- y1 y2 y1| j k y2| l m Matrix Z (Output) ---------------------------------------- x1y1 x1y2 x2y1 x2y2 x3y1 x3y2 x1y1| aj ak bj bk cj ck x1y2| al am bl

To learn PyMC, I'm trying to do a simple Hidden Markov Model as shown below: with pymc3.Model() as hmm: # Transition "matrix" a_t = np.ones(num_states) T = [pymc3.Dirichlet('T{0}'.format(i), a = a_t, shape = num_states) for i in xrange(num_states)] # Emission "matrix" a_e = np.ones(num_emissions) E = [pymc3.Dirichlet('E{0}'.format(i), a = a_e, shape = num_emissions) for i in xrange(num_states)] # State models p0 = np.ones(num_states) / num_states # No shape, so each state is a scalar tensor states = [pymc3.Categorical('s0', p = p0)] emissions = [pymc3.Categorical('z0', p = ifelse(eq(states[0],

The most alike question I found related to my question is this ( simple speech recognition methods ) but since had passed 3 years and the answers are not enough I will ask. I want to compute, from scratch, a simple speech recognition system, I only need to recognize five words. As much as I know, the more used audio features for this application are the MFCC, and HMM for classification. I'm able to extract the MFCC from audio but I still have some doubts about how to use the features for generating a model with HMM and then perform classification. As I understand, I have to perform vector