Jehoshaphat I. Abu
std::steam

std::steam

100 Days Of ML Code — Day 071

100 Days Of ML Code — Day 071

Jehoshaphat I. Abu's photo
Jehoshaphat I. Abu
·Sep 18, 2018·

2 min read

Recap From Day 070

Day 070, we looked at what these models have in common. You can catch up using the link below. 100 Days Of ML Code — Day 070 Recap From Day 069medium.com

Today, we will continue from where we left off in day 070.

Working with time

What these models have in common continued

The probability of what we are looking for if we take it for any kind of observation is what we call the prior. It’s our prior knowledge of what we are looking for and it is often defined by hand. Then the probability of what we observe given that we know what we are looking for is the likelihood because we already have a guess on what we are looking for and we test if the observation fit.

So the methods we’ve seen are both Bayesian and the Bayesian nature is intrinsically linked to the temporal structure. In this model, the belief we have in our estimation of the recognized gesture and all the potential variation is linked to the estimation we had at the last time which plays the role of the prior knowledge but taking into account the new incoming feature which is the new information on the gesture execution which is the likelihood. So the Bayesian rule is a way to start with an initial guess on which gesture the user will perform and which variations on that gesture and then constantly updating our belief based on the new observation.

As we can see, temporal modelling the fact that what happens at a given time also depends on what happened before is working well with the Bayesian hypothesis. Our belief on what happens now is based on our previous belief on what happened before. But considering now what is happening and this is important for real-time application as the belief of our estimation is constantly updated for each new observation and it’s pretty robust because beliefs are probability distribution over the possible values which allows the system to take into account values sources of noise such as the noise coming from the motion capture system.

That’s all for day 071. I hope you found this informative. Thank you for taking time out of your schedule and allowing me to be your guide on this journey. And until next time, be legendary.

References

*https://www.kadenze.com/courses/machine-learning-for-musicians-and-artists-v/sessions/working-with-time*

 
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