World population to reach 11 billion by 2100: UN study

UN Study titled World population stabilization unlikely this century has revealed that the World population will reach 11 billion by 2100 and it was highly unlikely that global population will stabilize by the end of 2100. 

On 18 September 2014, this study was published in the online edition of Journal Science.

The UN study projected the population growth on basis of data until 2012 and a Bayesian probabilistic methodology. 

Analysis of these data revealed that, there is an 80 percent probability that world population, now 7.2 billion, will increase to between 9.6 and 12.3 billion in 2100.

Highlights of the report:

• The World will have 2 billion more people on Earth than expected due to high birth rates in Africa.
• Sub-saharan Africa is anticipated to be the fastest growing region, with population to rise from one billion in 2014 to four billion by 2100. 
• There is an 80 percent chance that the population in Africa will be between 3.5 and 5.1 billion people by 2100.
• In Asia the population is projected to peak at around 5 billion people in 2050 and then start decline. 
• The North America, Europe, and Latin America and the Caribbean are all expected to be under one billion each.
• The issue of ageing population will also affect countries whose populations are very young today. Brazil, for example, currently has 8.6 people of working age for every person over 65, but that will fall to 1.5 by 2100, well below the current level in Japan. China and India will face the same issue as Brazil.

What is Bayesian probabilistic methodology?

Bayesian probability is one of the different interpretations of the concept of probability.

The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., thepropositions whose truth or falsity is uncertain.

The term "Bayesian" refers to the 18th century mathematician and theologian Thomas Bayes, who provided the first mathematical treatment of a non-trivial problem of Bayesian inference.

Mathematician Pierre-Simon Laplace pioneered and popularised what is now called Bayesian probability

Bayesian methods are characterized by the following concepts and procedures:

1. The use of random variables, or, more generally, unknown quantities, to model all sources of uncertainty in statistical models. This also includes uncertainty resulting from lack of information (see also the aleatoric and epistemic uncertainty).
2. The need to determine the prior probability distribution taking into account the available (prior) information.
3. The sequential use of the Bayes' formula: when more data become available, calculate the posterior distribution using the Bayes' formula; subsequently, the posterior distribution becomes the next prior.
4. For the frequentist a hypothesisis a proposition (which must be either true or false), so that the frequentist probability of a hypothesis is either one or zero. In Bayesian statistics, a probability can be assigned to a hypothesis that can differ from 0 or 1 if the truth value is uncertain.