What's the intuition behind the hidden states of RNN/LSTM? Are they similar to the hidden states of HMM (Hidden Markov Model)?

Say we've previously used a neural network or some other classifier C with $N$ training samples $I:=\{I_1,...I_N\}$ (that has a sequence or context, but is ignored by C) the, belonging to $K$ classes. Assume, for some reason (probably some training problem or declaring classes), C is confused and doesn't perform well. The way we assign a class using C to each test data $I$ is: $class(I):= arg max _{ {1 \leq j \leq K} } p_j(I)$, where $p_j(I)$ is the …

I have a dataset which looks like this: timestamp sensor1 sensor2 sensor3 sensor4 sensor5 action 1 0.05 0.04 0.10 0.39 0.59 A1 2 0.25 0.14 0.11 0.34 0.59 A2 3 0.15 0.34 0.13 0.36 0.59 A3 ....... Since I have the observations (sensor1-sensor5) and the corresponding labels (A1, A2, A3, etc.) for each timestamp, I want to perform supervised learning using a hidden markov model. Which library could I use to learn the observation distribution and the parameters of the …

I am trying to solve/understand ASR using HMM-GMM. At the abstract level i do understand what's happening but I did not understand how GMM fits into it. My data has 5K hours of speech from single user. I took the above picture from this article. I do know what is GMM but i am unable to wrap my head around it. Can somebody explain with a simple example.

I have data of each page visited by a customer in a session, my objective is to find out the most optimal path where we see the maximum conversion rate. My idea Is to use Markov Chain to identify that and probably use a mixture of Markov models to avoid bias towards any set of customers. Please let me know in case I am heading in the wrong direction.

Given that HMMs and Kalman Filters are both state space models with recursive aspects and KFs allow for control inputs to influence the prediction at each step, I'm wondering if any similar methods exist for HMMs. Let's say I have a 2-state HMM that takes continuous variables A and B as observations, but I know that variable C is associated with transitions. For instance, transitions to S1 (state 1) tend to be preceded by high values of C and transitions …

I am learning Hidden Markov Model and its implementation for Stock Price Prediction. I am trying to implement the Forward Algorithm according to this paper. Here I found an implementation of the Forward Algorithm in Python. import pandas as pd import numpy as np V = np.array([0, 1, 1, 2, 0, 1, 2, 0, 1, 0, 2]) # Transition Probabilities a = np.array(((0.54, 0.46), (0.49, 0.51))) # Emission Probabilities b = np.array(((0.16, 0.26, 0.58), (0.25, 0.28, 0.47))) # # Equal …

I would like to find some good courses but also a quick response on how to model transition matrix given the states. Imagine having 4 states and the following array [1,2,4,1,3,4,2 etc etc]. What calculations are possible with only an array of states? You can make the array as long as you want, I just gave a random exemple. Python ; Excel; Blog solutions are welcome

I want to train a sequence classifier with Hidden Markov Model. The length of observation sequences is not fixed. I tried some HMM packages such as Matlab's HMM toolbox and Kevin Murphy's library. All of them seem to require the user to specify the size of transition probability matrix and emission probability matrix. I understand that for a Hidden Markov Model (HMM), the sizes of the transition probability matrix and emission probability matrix are dependent on the number of hidden …

I am just getting started with Hidden Markov Models. In selecting my observable variables, there are some where I believe the recent change in the variable is potentially more predictive than its level. For example, in finance, the level of of an interest rate may not be as important as how much it has recently changed. Given that HMM presumes that only the present state matters, am I violating a best practice if I used the delta of variables in …

I am implementing a HMM (Hidden Markov Model) for time series data to assess (define) state of the model at each time period (with continuous observations). To maximize the likelihood of the model (given an observed data, the probability of hidden state the model is in at each data point) I used expectation maximization algorithm (in case of hmm - Baum-Welch algorithm). The problem is that in case of multidimensional data (the observation at each time is a vector), defined …

Given a set of sequence transitions, there are different orders of hidden markov models that can be fitted to a dataset. Is there any test to determine which is the best model for a given sequence set? How to compare two HMM fitted on to a sequence set?

Given a time series that is a blend of two ARMA(p, q) processes with a Markov process switching back and forth between the two, what would be the way to estimate the parameters? Is there a standard algorithm that I can implement in python?

HMM is a statistical model with unobserved (i.e. hidden) states used for recognition algorithms (speech, handwriting, gesture, ...). What distinguishes DHMM form CHMM is the transition probability matrix P with elements. In CHMM, state space of hidden variable is discrete and observation probabilities are modelled as Gaussian distributions. Why are observation probabilities modelled as Gaussian distributions in CHMM? Why are they (best)distributions for recognition systems in HMM?

I have gone through the concepts of HMM and I have understood most of them. However, I'm confused about how to map it to my problem. I have patients' information. Each patient is delivered a medicine after x weeks. x depends on patient's prescription. Now, each patient is delivered extra medication so if in any case delivery fails he/she don't miss their dose. Let's call this extra medication buffer. So a patient's buffer might be full which means that he …

I'm new to HMM and confused about it. I know it can be used to predict variable A at (t+1) if I give the past values of A. Face with multivariate time series, I have many series of variables A, B, C, ... and want to predict values of A in feature. It seems impossible in HMM, isn't right?

I've essentially been handed a dataset of website access history and I'm trying to draw some conclusions from it. The data supplied gives me the web URL, the datetime for when it was accessed, an the unique ID of the user accessing that data. This means that for a given user ID, I can see a timeline of how they went through the website and what pages they looked at. I'd quite like to try clustering these users into different …

My first question here. So I am trying to build a sign language translator(from signs to text) and noticed that the problem itself is quite similar to speech recognition, so I started to research about that. Right now one thing is I can't figure out is how exactly Hidden Markov models are used in speech recognition. I can understand how HMM can be used for example in part-of-speech tagging where we get a one of the states for each word. …

I have been trying to learn how to build ASRs and have been researching for awhile now, but I can't seem to get a straight answer. From what I understand, an ASR requires an Acoustic Model. That Acoustic Model can be trained via Baum-Welch or Viterbi training. Those algorithms train the parameters of a Hidden Markov Model. From what I gather, to train the parameters, we need the Wav files, from which the MFCC feature vectors can be obtained. On …

I am trying to find answers to the following questions. Can someone please help. This is a Hidden Markov Model with 7 states and 4 observations. I have worked out the following solution but still need help with parts ii & iii. Solution: I. GATTAG = 1* 1 * 0.5 * 0.25 * 0.2 * 0.5 * 0.4 * 0.15 * 0.6 * 0.25 * 1 * 0.5 * 1 =0.00005625 II. GTAAG possible paths: B -> S1-> S2 -> …