### lable Markov chains [3], sometimes referred to as dynamical systems [1]. To do so, particle filters require two types of information: data, and a probabilistic generative model of the system. The data generally comes in two flavors: measurements (e.g., camera images) and controls (e.g., robot motion commands). The measurement at time a2 will be denoted a3a5a4 ,

2001

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### Table 2.8: Comparisons of the total number of Markov chains on simulated data (in thou- sands).

2004

### Table 2.9: Comparisons of the total number of Markov chains on UCI data sets (in thou- sands).

2004

### Table 2: Regression Analysis of Supply Chain Integration with Organizational Performance

2002

"... In PAGE 8: ... Scores for efficiency, revenue growth and customer relationship performance were computed by aggregating items associated with each of these performance dimensions. Three regression models, one for each organizational performance measure, were analyzed and the results are presented in Table2... ..."

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### Table 3. Transitions from a state (k, n, m, r) in the Markov chain

2003

"... In PAGE 19: ...vents. For each event, it is possible to determine what state transitions can happen, i.e. what are the possible successor states of a generic state s = (k, n, m, r). This is what we discuss next, referring to Table3 which shows the conditions on the model state for a transition to be possible, the rate associated with the transition, and the successor state, for each type of... ..."

Cited by 8

### Table 1. Transitions from a state (k, n, m, r) in the Markov chain

2003

"... In PAGE 8: ...vents. For each event, it is possible to determine what state transitions can happen, i.e. what are the possible successor states of a generic state s = (k, n, m, r). This is what we discuss next, referring to Table1 which shows the conditions on the model state for a transition to be possible, the rate associated with the transition, and the successor state, for each type of events. Incoming GSM calls and handovers are accepted in the cell if the number of free channels, excluding those reserved as PDCHs, is such that the call can be accommodated.... ..."

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### Table 1 Markov chain of five states

"... In PAGE 2: ... Thus, we have two parallel sequences of Bernoulli trials, which are intermittently shifted forward. The shifting process can be described as a finite Markov chain apos; with the five states shown in Table1 .... ..."

### Table 2: Comparison of the multiple Markov chain simulation results for the proposed method (PSSM+SOV) and the standard Gibbs sampling method. Correct alignments are de ned when the number of correctly aligned sequences are equal to or larger than the cuto in the second column. The rate of correct alignments are obtained from multiple Markov chain simulations, 200 Markov chains for the proposed method (PSSM+SOV) and 100 Markov chains for the standard Gibbs sampling (PSSM alone). The number of Markov chains that nd the correct alignments are reported in the third and fourth columns.

2006

"... In PAGE 16: ... For this type of data, the predicted secondary structure enhances the motif pattern, therefore the true motif is easier to be identi ed under the new model. As demonstrated in Table2 , the proposed alignment method with secondary structure information nds the true motif of 1r69 much more frequently (3.85 more times) than the standard Gibbs sampling method.... In PAGE 16: ...ore frequently (3.85 more times) than the standard Gibbs sampling method. [Figure 3 about here.] Table2 shows comparisons of our proposed model with the standard Gibbs sampling method. For each data set, the alignments obtained by both methods are compared to the structural alignments in BAliBASE.... In PAGE 16: ... A good alignment is de ned when a large number of sequences out of the total number in each data set are correctly aligned. The criteria of determining good alignments are listed in the second column in Table2 . Multiple Markov chain simulations are used for the proposed method (PSSM+SOV) and the standard Gibbs sampling, where the proposed method runs 200 Markov chains, 50 runs at each of four =0:5; 1; 1:5; 2, and the standard Gibbs sampling runs 100 Markov chains.... In PAGE 16: ...5%. [ Table2 about here.] Further comparisons of the proposed method (PSSM+SOV) with ClustalW, Dialign, and PRRP are displayed in Table 3.... ..."

### Table 1: Markov chain based methods for PMS

1999

"... In PAGE 3: ...1 Literature survey In this section we present and compare the Markov chain-based approaches [2, 3, 5, 10, 16, 21, 22] and the one based on SAN in [4] for the dependability model- ing and analysis of PMS. The most relevant aspects of the comparison are summa- rized in Table1 .A key point that impacts most of the other aspects is represented by the sin- gle/separate modeling of the phases: it affects the reusability/flexibility of previously built models, the modeling of dependencies among phases and the complexity of the solution steps.... ..."

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### Table 2. Example for Deriving the States of the Markov Chains.

2004

"... In PAGE 6: ... That is, the sequence U consists of the indices of the elements in L corresponding to the job sequence. As an example, consider Table2 where the process of reducing... ..."

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