# ldhmm.gamma_init: Initializing tansition probability paramter In ldhmm: Hidden Markov Model for Financial Time-Series Based on Lambda Distribution

## Description

This utility has multiple purposes. It can generate a simple transition probability matrix, using p1 and p2, if prob is left as NULL. The generated gamma is raw and not normalized. If prob is provided as a vector, the utility converts it into a matrix as gamma. Furthermore, if prob is provided as a vector or matrix, the utility applies `min.gamma`, and normalize the sum of t.p.m. rows to 1. This is mainly an internal function used by MLE, not be concerned by external users.

## Usage

 `1` ```ldhmm.gamma_init(m, p1 = 0.04, p2 = 0.01, prob = NULL, min.gamma = 0) ```

## Arguments

 `m` numeric, number of states `p1` numeric, the first-neighbor transition probability, default is 0.4. `p2` numeric, the second-neighbor transition probability, default is 0.1. `prob` numeric or matrix, a full specified transition probability by user, default is `NULL`. If this is specified, p1, p2 would be ignored. `min.gamma` numeric, a minimum transition probability added to gamma to avoid singularity, default is 0. This is only used when `prob` is not `NULL`.

## Value

a matrix as gamma

Stephen H. Lihn

## Examples

 ```1 2 3 4 5 6``` ``` gamma0 <- ldhmm.gamma_init(m=3) prob=c(0.9, 0.1, 0.1, 0.1, 0.9, 0.0, 0.1, 0.1, 0.8) gamma1 <- ldhmm.gamma_init(m=3, prob=prob) gamma2 <- ldhmm.gamma_init(m=2, prob=gamma1, min.gamma=1e-6) ```

ldhmm documentation built on March 18, 2018, 1:51 p.m.