In the case of a dynamic Bayesian network, the probabilistic component is defined by a conditional probability chain rule and a Markov chain while the logic component is given by propositional logic with structural requirements.
The course of a chain reaction is determined by the probability that a neutron released in fission will cause a subsequent fission.
In the first scenario, SVC's modes are determined by the probability models, including conditional probability, Bayesian theorem, and Markov chain.
By repeating this process for every R-th point of our MCMC chain and averaging the result, we weight by the probability distribution of this predicted value.
This "probability of you having red if I have blue" is equal to "probability of me having blue if you have red, multiplied by the probability of you having red, divided by the probability of me having blue".
Applying the approach of Tan et al. to our system, the induced compaction of the micellar core by PTX reduces the probability of hydrophobic chains being exposed on the micelle surface, thus preventing the formation of wormlike structures.
By the chain rule of probabilities, the joint probability distribution on the random variables in the graph can be decomposed into a product of the local conditional probabilities, i.e., if there are n random variables x i, i = 1,2,…, n and Pa(X i ) denotes the parents of x i in the graph, then the joint probability distribution factors as (8) Pr (X 1, X 2, …, X n ) = ∏ i = 1 n Pr (X i | Pa (X i ) ).
Cook et al. [16] simulated linear chains as well as rosette-like structures in a dense system and found that the probability of inter-chain contacts decreases by the transition from a linear to a looping polymer.
The stationary distribution of the Markov chain is given by the normalized degree vector, and represents the probability of a Markov Chain residing in a particular node after infinite iterations.
