What is the Interpretation of Log Likelihood in Molecular Data Analysis?

In summary, a bioinformatician was discussing their use of a software that utilizes Hidden Markov Model and EM and Viterbi algorithms to calculate logL values for molecular data. The speaker was curious about how to interpret these values and asked for clarification. They later found the answer and thanked the other person.
  • #1
Phaso
2
0
Hello,

I am a Bio informatician and encountered Likelihood while executing the Molecular data. I have used one software that is using the Hidden Markov Model in addition to the EM Algorithm and Viterbi algorithm. After calculations are done already, in addition to the output, it is giving me some logL values.

The logL values are like below:

logL = -484.1534290649416

I just want to know how it is inferred? If it is very low in negative then the executed output is better or not? I have read some articles about log likelihood but they only explained about the whole likelihood process.

Note: There is no graph. It is only showing the logL values.

Good Day!
 
Physics news on Phys.org
  • #2
I found it... :)

Thanks!
 

What is log likelihood interpretation?

Log likelihood interpretation is a statistical method used to evaluate the fit of a model to a set of data. It measures the likelihood of observing the data given the estimated parameters of the model. A higher log likelihood value indicates a better fit.

How is log likelihood calculated?

Log likelihood is calculated by taking the natural logarithm of the likelihood function. The likelihood function is the probability of observing the data given the estimated parameters of the model. Therefore, the log likelihood is a measure of how probable it is to observe the data.

What is the difference between log likelihood and likelihood?

The main difference between log likelihood and likelihood is that log likelihood is a logarithmic transformation of the likelihood function. This is done to make the calculations easier and to avoid very small or large numbers. Log likelihood is often used in maximum likelihood estimation, while likelihood is used in Bayesian statistics.

How is log likelihood used in model selection?

Log likelihood is commonly used in model selection to compare the fit of different models to a set of data. The model with the highest log likelihood value is considered the best fit for the data. Log likelihood can also be used in conjunction with other statistical measures, such as AIC and BIC, to determine the most appropriate model.

What are the limitations of log likelihood interpretation?

One limitation of log likelihood interpretation is that it assumes that the data follows a specific probability distribution. If the data does not follow this distribution, the log likelihood values may not accurately reflect the fit of the model. Additionally, log likelihood does not take into account the complexity of a model, so it may not be the best measure for selecting a model with the fewest parameters.

Similar threads

Kalman Filter EM decrease log likelihood
    • Set Theory, Logic, Probability, Statistics
Replies
6
Views
3K
A Mixture density neural network prediction bias
    • Set Theory, Logic, Probability, Statistics
Replies
1
Views
984
ARMA/GARCH estimation with standard errors
    • Programming and Computer Science
Replies
1
Views
1K
B Viability of log-log transformation for some data
    • Set Theory, Logic, Probability, Statistics
Replies
1
Views
965
Correct Form of Likelihood Function for Data w/ Upper/Lower Bound
    • Set Theory, Logic, Probability, Statistics
Replies
8
Views
4K
I Pca and eigenvalue interpretation
    • Set Theory, Logic, Probability, Statistics
Replies
1
Views
1K
Need some help reading this graph and getting data from it (wind turbine simulation)
    • General Discussion
Replies
11
Views
781
Data analysis by guessing, checking and fixing
    • STEM Educators and Teaching
Replies
5
Views
634
Simple Logistic Regression
    • Set Theory, Logic, Probability, Statistics
Replies
1
Views
2K
A Schrödinger's original interpretation
    • Quantum Interpretations and Foundations
Replies
21
Views
2K

Hot Threads

Recent Insights

Back
Top