Table of Contents

- 1 What is the evaluation problem for hidden Markov model?
- 2 Which method is used to automatically estimate parameters of HMM?
- 3 What is Markov model explain hidden Markov model in machine learning?
- 4 Which reveals an improvement in online smoothing?
- 5 What are the model parameters used in observable Markov model?
- 6 What are the possible values of the variable in hidden Markov model?
- 7 How do you select the model with the lowest AIC?
- 8 What is the AIC value of a model with high log likelihood?

It is mostly used in speech recognition, to some extent it is also applied for classification task. HMM provides solution of three problems : evaluation, decoding and learning to find most likelihood classification.

**What is the approach used to resolve learning HMM problem from the given data?**

The predominant learning algorithm for Hidden Markov Models (HMMs) is local search heuristics, of which the Baum-Welch (BW) algorithm is mostly used. It is an iterative learning procedure starting with a predefined size of state spaces and randomly chosen initial parameters.

### Which method is used to automatically estimate parameters of HMM?

The standard HMM estimation algorithm (the Baum-Welch algorithm) was applied to update model parameters after each step of the GA. This approach uses the grammar (probabilistic modelling) of protein secondary structures and transfers it into the stochastic context-free grammar of an HMM.

**Which of the following are the correct elements used in Hidden Markov model?**

A HMM consists of two components. Each HMM contains a series of discrete-state, time-homologous, first-order Markov chains (MC) with suitable transition probabilities between states and an initial distribution.

The Hidden Markov model is a probabilistic model which is used to explain or derive the probabilistic characteristic of any random process. It basically says that an observed event will not be corresponding to its step-by-step status but related to a set of probability distributions.

**What is hidden Markov model in simple words?**

The Hidden Markov Model (HMM) is a relatively simple way to model sequential data. A hidden Markov model implies that the Markov Model underlying the data is hidden or unknown to you. More specifically, you only know observational data and not information about the states.

## Which reveals an improvement in online smoothing?

Matrix formulation

Which reveals an improvement in online smoothing? Explanation: Matrix formulation reveals an improvement in online smoothing with a fixed lag.

**How does Viterbi algorithm work?**

The Viterbi algorithm is a dynamic programming algorithm for obtaining the maximum a posteriori probability estimate of the most likely sequence of hidden states—called the Viterbi path—that results in a sequence of observed events, especially in the context of Markov information sources and hidden Markov models (HMM).

### What are the model parameters used in observable Markov model?

1. Speech recognition 2. Language modeling 3. Gesture recognition (e.g. sign language) 4.

**How do you train a hidden Markov model?**

One usually trains an HMM using an E-M algorithm. This consists of several iterations. Each iteration has one “estimate” and one “maximize” step. In the “maximize” step, you align each observation vector x with a state s in your model so that some likelihood measure is maximized.

Explanation: The possible values of the variables are the possible states of the world.

**What is Markov model used for?**

Markov models are often used to model the probabilities of different states and the rates of transitions among them. The method is generally used to model systems. Markov models can also be used to recognize patterns, make predictions and to learn the statistics of sequential data.

## How do you select the model with the lowest AIC?

The score, as defined above, is minimized, e.g. the model with the lowest AIC is selected. To use AIC for model selection, we simply choose the model giving smallest AIC over the set of models considered. — Page 231, The Elements of Statistical Learning, 2016.

**How do you calculate AIC in machine learning?**

AIC = -2/N * LL + 2 * k/N Where N is the number of examples in the training dataset, LL is the log-likelihood of the model on the training dataset, and k is the number of parameters in the model. The score, as defined above, is minimized, e.g. the model with the lowest AIC is selected.

### What is the AIC value of a model with high log likelihood?

AIC is low for models with high log-likelihoods (the model fits the data better, which is what we want), but adds a penalty term for models with higher parameter complexity, since more parameters means a model is more likely to overfit to the training data.

**What is the Akaike information criterion (AIC) value?**

The fifth column contains the Akaike information criterion (AIC) value. AIC compares the relative “quality” of a model (distribution) versus the other models. You can use AIC to select the distribution that best fits the data. The distribution with the smallest AIC value is usually the preferred model. AIC is defined as the following: