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

AI Thread Summary
Log likelihood (logL) values in molecular data analysis indicate the fit of a statistical model to the observed data. A lower negative logL value suggests a better model fit, as it indicates a higher likelihood of the observed data given the model. The user encountered logL values while using software that employs Hidden Markov Models and associated algorithms. Understanding log likelihood helps in model selection and evaluation, particularly in bioinformatics applications. Overall, lower negative logL values are preferred for optimal model performance.
Phaso
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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!
 
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I found it... :)

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Thread 'Formal derivation of statement from Peano Arithmetic system'

I was reading a Bachelor thesis on Peano Arithmetic (PA). PA has the following axioms (not including the induction schema): $$\begin{align} & (A1) ~~~~ \forall x \neg (x + 1 = 0) \nonumber \\ & (A2) ~~~~ \forall xy (x + 1 =y + 1 \to x = y) \nonumber \\ & (A3) ~~~~ \forall x (x + 0 = x) \nonumber \\ & (A4) ~~~~ \forall xy (x + (y +1) = (x + y ) + 1) \nonumber \\ & (A5) ~~~~ \forall x (x \cdot 0 = 0) \nonumber \\ & (A6) ~~~~ \forall xy (x \cdot (y + 1) = (x \cdot y) + x) \nonumber...
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