Joel Hasbrouck in his paper, “Security Bid/Ask Dynamics with Discreteness and Clustering” , uses Gibbs sampling for estimating the parameters of a stylized market microstructure model. For any model, there are many ways to estimate parameters. One of the common methods is the likelihood approach. Even though this approach makes sense intuitively, the computational complexity explodes as the number of parameters increase. The curse of dimensionality kicks in and hence parameters become notoriously unstable.

[youtube https://www.youtube.com/watch?v=r-ERajkMXw0?rel=0]

When I first encountered Kalman Filter technique, I was overwhelmed by the ton of approaches taken by various authors to explain it. It can be explained from an engineering vocabulary but I wanted to understand it from a stats point of view . One typically reads either the Frequentist approach( where Gaussian multivariate normal distribution is used to derive all the formulae) or the Bayesian approach where the usual prior-posterior stuff is used to derive Kalman Filter.

In today’s world, parents are extremely observant about how their children are learning. Be it academics or music or sport any other field that the child has developed a semblance of liking, the parent gives and seeks all the guidance available to make his/her kid’s learning process effective. Given the hyperconnected instant gratification world that we are all living it, Kids left to their own devices, become just that, in the literal sense.

[youtube https://www.youtube.com/watch?v=XNbaR54Gpj4?list=UUAuUUnT6oDeKwE6v1NGQxug]