Surprising Learning Approach in Mice Decoded, Study Reveals miragenews.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from miragenews.com Daily Mail and Mail on Sunday newspapers.
Neurotypical humans readily optimize performance in ‘reversal learning’ games, but while mice learn the winning strategy, they refuse to commit to it, a new MIT study shows. The research provides a mathematical way to track the rodents’ more mixed tactics.
Conditional particle filters (CPFs) with backward/ancestor sampling are powerful methods for sampling from the posterior distribution of the latent states of a dynamic model such as a hidden Markov model. However, the performance of these methods deteriorates with models involving weakly informative observations and/or slowly mixing dynamics. Both of these complications arise when sampling finely time-discretized continuous-time path integral models, but can occur with hidden Markov models too. Multinomial resampling, which is commonly employed with CPFs, resamples excessively for weakly informative observations and thereby introduces extra variance. Furthermore, slowly mixing dynamics render the backward/ancestor sampling steps ineffective, leading to degeneracy issues. We detail two conditional resampling strategies suitable for the weakly informative regime: the so-called “killing” resampling and the systematic resampling with mean partial order. To avoid the degeneracy issues,
Messy data brings a does of reality to analytics projects everywhere - it's a problem for AI projects as well. But can AI allow us to turn the messy data.