Probability Bracket Notation: Markov State Chain Projector, Hidden Markov Models and Dynamic Bayesian Networks

After a brief discussion of Markov Evolution Formula (MEF) expressed in Probability Bracket Notation (PBN), its close relation with the joint probability distribution (JPD) of Visible Markov Models (VMM) is demonstrated by introducing Markov State Chain Projector (MSCP). The state basis and the observed basis are defined in the Sequential Event Space (SES) of Hidden Markov Models (HMM). The JPD of HMM is derived by using basis transformation in SES. The Viterbi algorithm is revisited and applied to the famous Weather HMM example, whose node graph and inference results are displayed by using software package Elvira. In the end, the formulas of VMM, HMM and some factorial HMM (FHMM) are expressed in PBN as instances of dynamic Bayesian Networks (DBN).

Probability Bracket Notation, Multivariable Systems and Static Bayesian Networks

Probability Bracket Notation (PBN) is applied to systems of multiple random variables for preliminary study of static Bayesian Networks (BN) and Probabilistic Graphic Models (PGM). The famous Student BN Example is explored to show the local independences and reasoning power of a BN. Software package Elvira is used to graphically display the student BN. Our investigation shows that PBN provides a consistent and convenient alternative to manipulate many expressions related to joint, marginal and conditional probability distributions in static BN.