  In this talk, I argue that the method of analysing the m-z data of Type Ia
Supernovae (SNe Ia) by assuming exotic energy densities with strange equations
of state is misleading and the reasonable remaining option is to make a
model-independent analysis of SNe data, without reference to the energy
densities. Using this cosmographic method, we address an important question
relevant to cosmology: Was there a decelerating past for the universe? To
answer this, the Bayes's probability theory is employed, which is the most
appropriate tool for quantifying our knowledge when it changes through the
acquisition of new data. The cosmographic approach helps to sort out models
which were always accelerating from those which decelerated for at least some
time in the period of interest. Bayesian model comparison technique is used to
discriminate these rival hypotheses with the aid of recent releases of
supernova data. It is argued that the lessons learned using Bayesian theory are
extremely valuable to avoid frequent U-turns in cosmology.
