page_title: Gambling and information theory
section_title: Applications for self-information
hierarchical_section_title: Gambling and information theory / Applications for self-information
caption_attribution_description: Surprisal and evidence in bits as logarithmic measures of probability and odds, respectively.
context_page_description: Statistical inference might be thought of as gambling theory applied to the world around us. The myriad applications for logarithmic information measures tell us precisely how to take the best guess in the face of partial information. In that sense, information theory might be considered a formal expression of the theory of gambling. It is no surprise, therefore, that information theory has applications to games of chance.
context_section_description: The logarithmic probability measure self-information or surprisal, whose average is information entropy/uncertainty and whose average difference is KL-divergence, has applications to odds-analysis all by itself.  Its two primary strengths are that surprisals: (i) reduce minuscule probabilities to numbers of manageable size, and (ii) add whenever probabilities multiply.
  For example, one might say that "the number of states equals two to the number of bits" i.e. #states = 2#bits.  Here the quantity that's measured in bits is the logarithmic information measure mentioned above.  Hence there are N bits of surprisal in landing all heads on one's first toss of N coins.
  The additive nature of surprisals, and one's ability to get a feel for their meaning with a handful of coins, can help one put improbable events (like winning the lottery, or having an accident) into context.  For example if one out of 17 million tickets is a winner, then the surprisal of winning from a single random selection is about 24 bits.  Tossing 24 coins a few times might give you a feel for the surprisal of getting all heads on the first try.
  The additive nature of this measure also comes in handy when weighing alternatives.  For example, imagine that the surprisal of harm from a vaccination is 20 bits.  If the surprisal of catching a disease without it is 16 bits, but the surprisal of harm from the disease if you catch it is 2 bits, then the surprisal of harm from NOT getting the vaccination is only 16+2=18 bits.  Whether or not you decide to get the vaccination (e.g. the monetary cost of paying for it is not included in this discussion), you can in that way at least take responsibility for a decision informed to the fact that not getting the vaccination involves more than one bit of additional risk.
  More generally, one can relate probability p to bits of surprisal sbits as probability = 1/2ˢᵇⁱᵗˢ.  As suggested above, this is mainly useful with small probabilities.  However, Jaynes pointed out that with true-false assertions one can also define bits of evidence ebits as the surprisal against minus the surprisal for.  This evidence in bits relates simply to the odds ratio = p/(1-p) = 2ᵉᵇⁱᵗˢ, and has advantages similar to those of self-information itself.
caption: Surprisal and evidence in bits, as logarithmic measures of probability and odds respectively.
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