Vtyjkyuk

I am reading Gaussian Distribution from a machine learning book. It states that -

We shall determine values for the unknown parameters $mu$ and
$sigma^2$ in the Gaussian by maximizing the likelihood function. In practice, it is more convenient to maximize the log of the likelihood
function. Because the logarithm is monotonically increasing function
of its argument, maximization of the log of a function is equivalent
to maximization of the function itself. Taking the log not only
simplifies the subsequent mathematical analysis, but it also helps
numerically because the product of a large number of small
probabilities can easily underflow the numerical precision of the
computer, and this is resolved by computing instead the sum of the log
probabilities.

can anyone give me some intuition behind it with some example? Where the log likelihood is more convenient over likelihood. Please give me a practical example.

Thanks in advance!

1. It is extremely useful for example when you want to calculate the
   joint likelihood for a set of independent and identically distributed points. Assuming that you have your points:
   $$X=x_1,x_2,ldots,x_N $$ The total likelihood is the product of
   the likelihood for each point, i.e.:
   $$p(XmidTheta)=prod_i=1^Np(x_imidTheta) $$ where $Theta$ are the
   model parameters: vector of means $mu$ and covariance matrix
   $Sigma$. If you use the log-likelihood you will end up with sum
   instead of product: $$ln p(XmidTheta)=sum_i=1^Nln
   p(x_imidTheta) $$


2. 
   

   Also in the case of Gaussian, it allows you to avoid computation of
   the exponential:

   
   
   

   $$p(xmidTheta) =
   dfrac1(sqrt2pi)^dsqrtdetSigmae^-frac12(x-mu)^T Sigma^-1(x-mu)$$
   Which becomes:

   
   
   

   $$ln p(xmidTheta) = -fracd2ln(2pi)-frac12ln(det
   Sigma)-frac12(x-mu)^TSigma^-1(x-mu)$$

   
   


3. 
   

   Like you mentioned $ln x$ is a monotonically increasing function,
   thus log-likelihoods have the same relations of order as the
   likelihoods:

   
   
   

   $$p(xmidTheta_1)>p(xmidTheta_2) Leftrightarrow ln
   p(xmidTheta_1)>ln p(xmidTheta_2)$$

   
   


4. 
   

   From a standpoint of computational complexity, you can imagine that first
   of all summing is less expensive than multiplication (although nowadays these are almost equal). But what is
   even more important, likelihoods would become very small and you
   will run out of your floating point precision very quickly, yielding
   an underflow. That's why it is way more convenient to use the
   logarithm of the likelihood. Simply try to calculate the likelihood by hand, using pocket calculator - almost impossible.

   
   
   

   Additionally in the classification framework you can simplify
   calculations even further. The relations of order will remain valid
   if you drop the division by $2$ and the $dln(2pi)$ term. You can do
   that because these are class independent. Also, as one might notice if
   variance of both classes is the same ($Sigma_1=Sigma_2 $), then
   you can also remove the $ln(det Sigma) $ term.

   
   

First of all as stated, the log is monotonically increasing so maximizing likelihood is equivalent to maximizing log likelihood. Furthermore, one can make use of $ln(ab) = ln(a) + ln(b)$. Many equations simplify significantly because one gets sums where one had products before and now one can maximize simply by taking derivatives and setting equal to $0$.