Recency-weighted Markovian inference
نویسنده
چکیده
We describe a Markov latent state space (MLSS) model, where the latent state distribution is a decaying mixture over multiple past states. We present a simple sampling algorithm that allows to approximate such high-order MLSS with fixed time and memory costs.
منابع مشابه
ROBUSTNESS OF THE TRIPLE IMPLICATION INFERENCE METHOD BASED ON THE WEIGHTED LOGIC METRIC
This paper focuses on the robustness problem of full implication triple implication inference method for fuzzy reasoning. First of all, based on strong regular implication, the weighted logic metric for measuring distance between two fuzzy sets is proposed. Besides, under this metric, some robustness results of the triple implication method are obtained, which demonstrates that the triple impli...
متن کاملMemory in Chains: Modeling Primacy and Recency Effects in Memory for Order
Memory for order is fundamental in everyday cognition, supporting basic processes like causal inference. However, theories of order memory are narrower, if anything, than theories of memory generally. The memory-in-chains (MIC) model improves on existing theories by explaining a family of order memory effects, by explaining more processes, and by making strong predictions. This paper examines t...
متن کاملRecency, Consistent Learning, and Nash Equilibrium Learning with Recency Bias
We examine the long-run implication of two models of learning with recency bias: recursive weights and limited memory. We show that both models generate similar beliefs, and that both have a weighted universal consistency property. Using the limited memory model we are able to produce learning procedures that are both weighted universally consistent and converge with probability one to strict N...
متن کاملRecency, consistent learning, and Nash equilibrium.
We examine the long-term implication of two models of learning with recency bias: recursive weights and limited memory. We show that both models generate similar beliefs and that both have a weighted universal consistency property. Using the limited-memory model we produce learning procedures that both are weighted universally consistent and converge with probability one to strict Nash equilibr...
متن کاملLearning with Recency Bias
We examine the long-run implication of two models of learning with recency bias: recursive weights and limited memory. We show that both models generate similar beliefs, and that both have a weighted universal consistency property. Using the limited memory model we are able to produce learning procedures that are both weighted universally consistent and converge with probability one to strict N...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- CoRR
دوره abs/1711.03038 شماره
صفحات -
تاریخ انتشار 2017