Description Usage Arguments Details Value See Also Examples

`backward`

calculates the backward probabilities for all the nodes

1 |

`hmm` |
hmm Object of class List given as output by |

`observation` |
A list consisting "k" vectors for "k" features, each vector being a character series of discrete emmision values at different nodes serially sorted by node number |

`bt_seq` |
A vector denoting the order of nodes in which the tree should be traversed in backward direction(from leaves to roots). Output of |

`kn_states` |
(Optional) A (L * 2) dataframe where L is the number of training nodes where state values are known. First column should be the node number and the second column being the corresponding known state values of the nodes |

The backward probability for state X and observation at node k is defined as the probability of observing the sequence of observations e_k+1, ... ,e_n under the condition that the state at node k is X.
That is:`b[X,k] := Prob(E_k+1 = e_k+1, ... , E_n = e_n | X_k = X)`

where `E_1...E_n = e_1...e_n`

is the sequence of observed emissions and `X_k`

is a random variable that represents the state at node `k`

(N * D) matrix denoting the backward probabilites at each node of the tree, where "N" is possible no. of states and "D" is the total number of nodes in the tree

1 2 3 4 5 6 7 8 | ```
tmat = matrix(c(0,0,1,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0),
5,5, byrow= TRUE ) #for "X" (5 nodes) shaped tree
hmmA = initHMM(c("P","N"),list(c("L","R")), tmat) #one feature with two discrete levels "L" and "R"
obsv = list(c("L","L","R","R","L")) #emissions for the one feature for the 5 nodes in order 1:5
bt_sq = bwd_seq_gen(hmmA)
kn_st = data.frame(node=c(3),state=c("P"),stringsAsFactors = FALSE)
#state at node 3 is known to be "P"
BackwardProbs = backward(hmmA,obsv,bt_sq,kn_st)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.