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When I calculate the log likelihood function, I found that the values is positive. So, is that ok. Can the log likelihood function be positive? … To be more clear: $ll = \sum_{n=1}^{N}\log(\sum_{k=1}^{K} \pi_{k} f(x_n;\theta_{k}))$ …

I am currently doing a multiclass classification task on sequence data and am using tf.contrib.crf.crf_log_likelihood to compute sentence level log-likelihood values. … After training for roughly 2 epochs the maximum log-likelihood value (over the batch) starts to become positive. I am wondering how this could happen? Would this not entail a probability over 1? …

When I estimated the AR(1)- GARCH(1,1) using log returns, the value of the log-likelihood is positive (see bellow): Standard t Parameter Value … 0.776569 0.00637141 121.883 ARCH{1} 0.223431 0.0103645 21.5574 logL = -6.5997e+03 The change of the volatility level makes sense for me, but why the value of the log-likelihood …

This distribution can have density > 1, and so when I compute log likelihood of the data, I sometimes get positive values (meaning likelihood was > 1). … Just report the positive log likelihood? Or do they truncate or try to change it in some other way so LL is in (-infinity, 0]? …

My problem is that in some cases the log-likelihood for the model of interest (and sometimes also the null model) is positive. … If not, is there a different method for calculating reasonable pseudo-R2 for models with positive log likelihoods? Thank you, in advance, for any advice. Laura Perry …

The log- likelihood=93.69 is positive which is unusual. It is clear for me that the log-likehood is not as same as the probability. But how can this originate from the analysis? … 4.346176,4.395557, 4.442923, 4.497221, 4.561284, 4.626783, 4.659033, 4.643283, 4.705451, 4.774832, 4.814009, 4.826510,4.859228) result of density function given observation dnorm(tx ,mean(tx), sd(tx),log …

All my models have positive -2*LL (log likelihood) values which as far as I understand is expected under certain circumstances and not much of a problem... ...unless - and this is just me wondering - … With large positive -2LL there appears little penalty for more complex models, which suddenly rank surprisingly high. Can I still use AIC for model comparison? Thanks heaps!! …

However, I got all the positive value after calculating the log-likelihood. I just want to know am I doing right? I use the log-likelihood for the feature selection for unigrams. … Here is the code for calculating log-likelihood: # Feature Selection Log-Likelihood function def cal_LogLikelihood(features_value): n = len(features_value) mean = np.mean(features_value) var = np.var …

We can approximate the log likelihood at the real parameter value $l(\theta)$ with the MLE estimate $l(\hat\theta)$ using second order Taylor polynomials, like so: $$l(\theta) - l(\hat\theta) \approx … This says that the log likelihood is higher under the MLE estimate than the true parameter value - shouldn't the truth be the highest attainable likelihood? …

I am calculating the log-likelihood of multivariate Gaussian distribution. I am getting a positive log-likelihood. … Log-likelihood should be a negative number. What is wrong? Thanks! …

log-likelihood function which is all that the entropy formula seems to do (see above). … Also not sure whether positive or negative log-likelihood is more comparable to entropy. …

I have the following 2 questions about log-likelihood computation in semi-supervised Naive Bayes. … I have read on several documents online that, in every EM iteration of the semi-supervised Naive Bayes, log-likelihood is positive. Is this always true? …

Can the log likelihood ratio for a simple vs simple hypothesis take a negative value? … The log likelihood ratio test statistic in this case is $$-2Log\Lambda$$ …

55 55 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" My question is why do I get a negative $value (-14.27772) for as the maximum value of negative log-likelihood … because log-likelihood suppose to be a negative value so, maximum value of negative log-likelihood has to be a positive isn't it?. Appreciate your comments. …

I found that the log-likelihood has a negative value. For example, I have this: -34.5. Then, when I count the AIC, I will get, a positive value for AIC. …