• No results found

18 results with keyword: 'formal reasoning finite state discrete time markov chains'

Formal Reasoning About Finite-State Discrete-Time Markov Chains in HOL

This small 2-state DTMC case study clearly illustrates the main strength of the proposed theorem proving based technique against the probabilistic model checking [23] approach

Protected

N/A

23
0
0
2021
The power of averaging at two consecutive time steps: Proof of a mixing conjecture by Aldous and Fill

In this work we prove that given a sequence of irreducible reversible finite discrete-time Markov chains, the sequence of associated continuous-time chains exhibits

Protected

N/A

13
0
0
2021
PARAMETER SYNTHESIS FOR CONTINUOUS-TIME MARKOV CHAINS

An interesting subset of Markov chains are Continuous-Time Markov Chains (CTMCs), which are used to model and analyse models with finite state space and their evolution over time..

Protected

N/A

34
0
0
2021
APTS Applied Stochastic Processes Preliminary material. Introduction. Learning Outcomes. Introduction

Finite state-space discrete Markov chains have a useful simplifying property: they are always positive-recurrent if they are irreducible2. This argument fails for infinite

Protected

N/A

12
0
0
2022
Strategy selection and outcome prediction in sport using dynamic learning for stochastic processes

For discrete-time situations with discrete outcomes, some useful stochastic processes identi fi ed are Bernoulli processes, discrete-time Markov chains and hidden Markov models..

Protected

N/A

11
0
0
2020
Strategy selection and outcome prediction in sport using dynamic learning for stochastic processes

For discrete-time situations with discrete outcomes, some useful stochastic processes identi fied are Bernoulli processes, discrete-time Markov chains and hidden Markov models..

Protected

N/A

10
0
0
2021
Discrete Time Markov Chains with R

validate the input transition matrix, plot the transition matrix as a graph diagram, perform structural analysis of DTMCs (e.g. classification of transition matrices and

Protected

N/A

21
0
0
2022
Exchange & Visiting AY Office of Academic Links

The incoming exchange/visiting students will receive an updated list of courses with outlines in July and apply for courses online in August if the students study with us in

Protected

N/A

15
0
0
2022
Discrete time Markov chains with interval probabilities

The following lemma shows that in the case where the set of transition matrices contains at least one regular matrix there exists a unique minimal closed invariant set with respect

Protected

N/A

16
0
0
2021
Labor supply, biased technological change and economic growth

The model we present is consistent with the second explanation. The negative shift in labor supply increases the relative abundance of capital and generates an incentive for

Protected

N/A

27
0
0
2021
Biased technological change, human capital and factor shares

Under these conditions the share of human factors (1 ) remains constant if labor saving innovations are always human capital using and land saving innovations are always

Protected

N/A

29
0
0
2021
Learning Discrete-Time Markov Chains Under Concept Drift

The choice of K defines a trade-off between false positive detections and detection delay in both the parametric and non- parametric mechanism detailed above. Following the

Protected

N/A

13
0
0
2021
Finite Markov chains

On one hand, as 0 and N are absorbing states, we have that these states are essential. On the other hand, the states 1,. , N − 1 are passing through states because it is possible

Protected

N/A

64
0
0
2021
Covariance ordering for discrete andcontinuous time Markov chains

One is Peskun ordering (1973), extended by Tierney (1998) to general state spaces, and the other is the covariance ordering introduced by Mira and Geyer (1999).. Ordering Markov

Protected

N/A

16
0
0
2021
PEARL C. AKANWA EMMANUEL UWAZIE ANYANWU

Population is important in any society because the growth rate has implications for the country, state, town, city, and even the village... Commission to ascertain the

Protected

N/A

15
0
0
2021
The strong deviation theorem for discrete time and continuous state nonhomogeneous Markov chains

In this paper, by using the notion of asymptotic log-likehood and the martingale conver- gence theorem, and extending the analytic technique proposed by Liu [], Liu and Yang [] to

Protected

N/A

8
0
0
2020
On the mixing property and the ergodic principle for nonhomogeneous Markov chains

We study different types of limit behavior of infinite dimension discrete time nonhomogeneous Markov chains.. We show that the geometric structure of the set of those Markov

Protected

N/A

14
0
0
2021
Stephane Crepey. Financial Modeling. A Backward Stochastic Differential Equations Perspective. 4y Springer

1 Some Classes of Discrete-Time Stochastic Processes 3 1.1 Discrete-Time Stochastic Processes 3 1.1.1 Conditional Expectations and Filtrations 3 1.2 Discrete-Time Markov Chains 6

Protected

N/A

8
0
0
2021

Upload more documents and download any material studies right away!