Markov Models
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Paperback. Condition: new. Paperback. What is a MEMORYLESS predictive model? Markov models are a powerful predictive technique used to model stochastic systems using time-series data. They are centered around the fundamental property of "memorylessness", stating that the outcome of a problem depends only on the current state of the system - historical data must be ignored. This model construction may sound overly simplistic. After all, if you have historical data why not use it to develop more complete and well-informed models? Surely, it would lead to more accurate predictions. However, when modelling time-series data where previous results are of limited relevance, a memoryless model delivers vast performance advantages. By considering only the present state, algorithms become highly scalable, stable, fast and, above-all-else, extremely versatile. Speech recognition is a perfect example - nearly all of today's speech recognition algorthms are built using Markov Models. In this book we will explore why a Memoryless predictive model can be so advantageous to the modern tech industry. We will take a look at fundamental mathematics and high-level concepts alike, extending our understanding of the subject beyond the simple Markov Model. You will learn. Foundations of Markov ModelsMarkov ChainsCase Study: Google PageRankHidden Markov ModelsBayesian NetworksInference Tasks This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781978304871
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