62Mxx Inference from stochastic processes
This subtopic introduces the core ideas in inference from stochastic processes, including foundational concepts, standard methods, and the main questions used to organize the area. Typical uses include building mathematical background, framing related research problems, and supporting applications in neighboring fields where these concepts provide useful structure.
Specific topics
62M02 Markov processes: hypothesis testing
Overview
Markov processes: hypothesis testing. This topic covers inference from stochastic processes, including time series, spatial processes, and dependent data models.
Related Wikipedia Page
Wikipedia: Time series
Useful Links
Key Ideas
- dependence-aware inference
- time/space process modeling
- spectral and state-space methods
Typical Uses
Used to infer structure and parameters from correlated observations indexed by time or space.
Applications
- Econometrics and forecasting
- Environmental and geostatistical modeling
- Signal processing and control
References
Recommended Textbooks
62M05 Markov processes: estimation
Overview
Markov processes: estimation. This topic covers inference from stochastic processes, including time series, spatial processes, and dependent data models.
Related Wikipedia Page
Wikipedia: Time series
Useful Links
Key Ideas
- dependence-aware inference
- time/space process modeling
- spectral and state-space methods
Typical Uses
Used to infer structure and parameters from correlated observations indexed by time or space.
Applications
- Econometrics and forecasting
- Environmental and geostatistical modeling
- Signal processing and control
References
Recommended Textbooks
62M07 Non-Markovian processes: hypothesis testing
Overview
Non-Markovian processes: hypothesis testing. This topic covers inference from stochastic processes, including time series, spatial processes, and dependent data models.
Related Wikipedia Page
Wikipedia: Time series
Useful Links
Key Ideas
- dependence-aware inference
- time/space process modeling
- spectral and state-space methods
Typical Uses
Used to infer structure and parameters from correlated observations indexed by time or space.
Applications
- Econometrics and forecasting
- Environmental and geostatistical modeling
- Signal processing and control
References
Recommended Textbooks
62M09 Non-Markovian processes: estimation
Overview
Non-Markovian processes: estimation. This topic covers inference from stochastic processes, including time series, spatial processes, and dependent data models.
Related Wikipedia Page
Wikipedia: Time series
Useful Links
Key Ideas
- dependence-aware inference
- time/space process modeling
- spectral and state-space methods
Typical Uses
Used to infer structure and parameters from correlated observations indexed by time or space.
Applications
- Econometrics and forecasting
- Environmental and geostatistical modeling
- Signal processing and control
References
Recommended Textbooks
62M10 Time series, auto-correlation, regression, etc.
Overview
Time series, auto-correlation, regression, etc.. This topic covers inference from stochastic processes, including time series, spatial processes, and dependent data models.
Related Wikipedia Page
Wikipedia: Time series
Useful Links
Key Ideas
- dependence-aware inference
- time/space process modeling
- spectral and state-space methods
Typical Uses
Used to infer structure and parameters from correlated observations indexed by time or space.
Applications
- Econometrics and forecasting
- Environmental and geostatistical modeling
- Signal processing and control
References
Recommended Textbooks
62M15 Inference from stochastic processes and spectral analysis
Overview
Inference from stochastic processes and spectral analysis. This topic covers inference from stochastic processes, including time series, spatial processes, and dependent data models.
Related Wikipedia Page
Wikipedia: Time series
Useful Links
Key Ideas
- dependence-aware inference
- time/space process modeling
- spectral and state-space methods
Typical Uses
Used to infer structure and parameters from correlated observations indexed by time or space.
Applications
- Econometrics and forecasting
- Environmental and geostatistical modeling
- Signal processing and control
References
Recommended Textbooks
62M20 Inference from stochastic processes and prediction
Overview
Inference from stochastic processes and prediction. This topic covers inference from stochastic processes, including time series, spatial processes, and dependent data models.
Related Wikipedia Page
Wikipedia: Time series
Useful Links
Key Ideas
- dependence-aware inference
- time/space process modeling
- spectral and state-space methods
Typical Uses
Used to infer structure and parameters from correlated observations indexed by time or space.
Applications
- Econometrics and forecasting
- Environmental and geostatistical modeling
- Signal processing and control
References
Recommended Textbooks
62M30 Inference from spatial processes
Overview
Inference from spatial processes. This topic covers inference from stochastic processes, including time series, spatial processes, and dependent data models.
Related Wikipedia Page
Wikipedia: Time series
Useful Links
Key Ideas
- dependence-aware inference
- time/space process modeling
- spectral and state-space methods
Typical Uses
Used to infer structure and parameters from correlated observations indexed by time or space.
Applications
- Econometrics and forecasting
- Environmental and geostatistical modeling
- Signal processing and control
References
Recommended Textbooks
62M40 Statistics of random processes
Overview
Statistics of random processes. This topic covers inference from stochastic processes, including time series, spatial processes, and dependent data models.
Related Wikipedia Page
Wikipedia: Time series
Useful Links
Key Ideas
- dependence-aware inference
- time/space process modeling
- spectral and state-space methods
Typical Uses
Used to infer structure and parameters from correlated observations indexed by time or space.
Applications
- Econometrics and forecasting
- Environmental and geostatistical modeling
- Signal processing and control
References
Recommended Textbooks
62M45 Neural nets and related approaches to inference
Overview
Neural nets and related approaches to inference. This topic covers inference from stochastic processes, including time series, spatial processes, and dependent data models.
Related Wikipedia Page
Wikipedia: Time series
Useful Links
Key Ideas
- dependence-aware inference
- time/space process modeling
- spectral and state-space methods
Typical Uses
Used to infer structure and parameters from correlated observations indexed by time or space.
Applications
- Econometrics and forecasting
- Environmental and geostatistical modeling
- Signal processing and control
References
Recommended Textbooks