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What is Learnable in Valiant's Theory of the Learnable? Learning Perturbations to Extrapolate Your LLM Byzantine-Robust Distributed Sparse Learning Revisited The Sample Complexity of Multiple Change Point Identification under Bandit Feedback A proximal gradient algorithm for composite log-concave sampling Model-based Bootstrap of Controlled Markov Chains Approximation of Maximally Monotone Operators : A Graph Convergence Perspective Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound A Spectral Framework for Closed-Form Relative Density Estimation Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation Scaling Limits of Long-Context Transformers A Note on Non-Negative $L_1$-Approximating Polynomials Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning 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for Kernel Gradient Flow Estimator A renormalization-group inspired lattice-based framework for piecewise generalized linear models Direct Estimation of Schrödinger Bridge Time-Series Drifts: Finite-Sample, Asymptotic, and Adaptive Guarantees Information-theoretic Limits of Learning and Estimation Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning Multiscale Euclidean Network Trajectories: Second-Moment Geometry, Attribution, and Change Points Causal discovery under mean independence and linearity Perturbation is All You Need for Extrapolating Language Models Realizable Bayes-Consistency for General Metric Losses Vanishing L2 regularization for the softmax Multi Armed Bandit Imbalanced Classification under Capacity Constraints Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy On the Optimal Sample Complexity of Offline Multi-Armed Bandits with KL Regularization Extrapolation in Statistical Learning 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On Success runs of a fixed length defined on a $q$-sequence of binary trials
Jungtaek Oh, Dae-Gyu Jang · 2022-10-10 · via math.ST updates on arXiv.org

We study the exact distributions of runs of a fixed length in variation which considers binary trials for which the probability of ones is geometrically varying. The random variable $E_{n,k}$ denote the number of success runs of a fixed length $k$, $1\leq k \leq n$. Theorem 3.1 gives an closed expression for the probability mass function (PMF) of the Type4 $q$-binomial distribution of order $k$. Theorem 3.2 and Corollary 3.1 gives an recursive expression for the probability mass function (PMF) of the Type4 $q$-binomial distribution of order $k$. The probability generating function and moments of random variable $E_{n,k}$ are obtained as a recursive expression. We address the parameter estimation in the distribution of $E_{n,k}$ by numerical techniques. In the present work, we consider a sequence of independent binary zero and one trials with not necessarily identical distribution with the probability of ones varying according to a geometric rule. Exact and recursive formulae for the distribution obtained by means of enumerative combinatorics.

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