Why do we determine the values of λ in regularization as ln λ, such as ln λ=-18 instead of for example λ=0.3? I'm studying Pattern Recognition and Machine Learning by Christopher Bishop. What I realized is, he defines values of λ as ln λ. For example:


  We see that, for a value of lnλ = −18, the over-fitting has been suppressed and we
  now obtain a much closer representation of the underlying function sin(2πx). If,
  however, we use too large a value for λ then we again obtain a poor fit, as shown in
  Figure 1.7 for lnλ = 0


What is the reason for this? Why he doesn't just use λ?
I can just suppose, that it's because then we could consider regularization together with a Log Likelihood function, for instance, and that is why it's more convenient to have such a representation. It's easier to make computations, using log, for example, when we want to minimize some functions (ln a + ln b = ln(a*b), (ln a)' = 1/a etc)