Understanding Temporal Models
Temporal models are used to represent probabilistic relationships between sequences of random variables that change over time. These models capture the dynamics and dependencies of data points within a sequence, allowing for the prediction and analysis of future states based on past and present observations.
Key Components of Temporal Models:
- States: These represent the possible conditions of the system at different times.
- Observations: These are the data points that are directly measured or perceived.
- Transitions: These are the probabilities from one state to another over time.
- Emissions: These are the probabilities of observing certain data given the system’s state.
Inference in Temporal Models
Temporal models play a crucial role in analyzing and predicting time-dependent phenomena. They capture dynamic relationships and dependencies between variables over time, making them indispensable in fields like finance, healthcare, and climate science. Inference in temporal models involves estimating hidden states, model parameters, and future observations based on observed data. This article provides an overview of temporal models, the methods used for inference, the associated challenges, and practical applications.
Table of Content
- Understanding Temporal Models
- Types of Temporal Models
- Inference Methods for Temporal Models
- 1. Filtering
- 2. Smoothing
- 3. Prediction
- 4. Most Likely Sequence (Viterbi Algorithm)
- Challenges in Inference for Temporal Models
- Application of Inference in Temporal Models
- Conclusion
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