How to interprets this algorithm

I make some algorithms to estimate the optimal values.

and i want to model it algorithms using probabilistic theorem.

but i dont know which algorithm or model is proper expression.

at first, i thinks it is a kind of bayesian network.

When a given graphical model is A -> B.

In typical bayesian networks,

P(A,B) = P(B|A)P(A)

And before estimates probability, we calcualte P(B|A) and P(A).

But in my case,

I have to calculate P(A) first

and when i estimate P(B|A), I use A' = arg max_A { P(A) } ex. P(B|A) can be N(B, A') ; A' is mean.

It feels something iterative, update or sequantial.

This property makes me interpret this algorithm hard.

I find below theorem or algorhtms.

bayesian network, markov chain, kalman filter, reculsive bayesian estimator. ...

In common sense, when estimate next probability, using a value that maximizes prior probability makes sense.

But how i can interprets it.

Please help me.