# Preprints

- J. Franks, A. Jasra, K. Law and M. Vihola “Unbiased inference for discretely observed hidden Markov model diffusions” 2018.
- A. Lee, S. S. Singh and M. Vihola “Coupled conditional backward sampling particle filter,” 2018.
- J. Franks and M. Vihola “Importance sampling correction versus standard averages of reversible MCMCs in terms of the asymptotic variance,” 2017.
- M. Vihola, J. Helske and J. Franks “Importance sampling type estimators based on approximate marginal MCMC,” 2016.

# Journal articles

- M. Vihola and J. Franks
“On the use of approximate Bayesian computation Markov chain
Monte Carlo with inflated tolerance and
post-correction”
*Biometrika*, to appear. - M. Vihola
“Unbiased estimators and multilevel Monte Carlo,”
*Operations Research*, 66(2):448–462, 2018. doi:10.1287/opre.2017.1670 - L. Leskelä and M. Vihola
“Conditional convex orders and measurable martingale
couplings,”
*Bernoulli*, 23(4A):2784–2807, 2017. doi:10.3150/16-BEJ827Preprint: arXiv:1404.0999 - G. Fort,
E. Moulines,
A. Schreck
and M. Vihola
“Convergence of Markovian stochastic approximation with
discontinuous dynamics,”
*SIAM Journal on Control and Optimization*. 54(2):866–893, 2016. doi:10.1137/140962723Preprint: arXiv:1403.6803 - C. Andrieu and M. Vihola
“Establishing some order amongst exact approximations of MCMCs,”
*Annals of Applied Probability*. 26(5):2661–2696, 2016. doi:10.1214/15-AAP1158Preprint: arXiv:1404.6909 - C. Andrieu, A. Lee and M. Vihola
“Uniform ergodicity of the iterated conditional SMC and geometric
ergodicity of particle Gibbs samplers,”
*Bernoulli*24(2), 842–872, 2018. doi:10.3150/15-BEJ785Preprint: arXiv:1312.6432 - I. S. Mbalawata, S.
Särkkä, M. Vihola and H. Haario
“Adaptive Metropolis algorithm using variational Bayesian adaptive
Kalman filter,”
*Computational Statistics & Data Analysis*, 83(3):101–115, 2015. doi:10.1016/j.csda.2014.10.006Preprint: arXiv:1308.5875 - C. Andrieu, G. Fort and M. Vihola
“Quantitative convergence rates for sub-geometric Markov chains,”
*Journal of Applied Probability*, 52(2):391–404, 2015. doi:10.1239/jap/1437658605Preprint: arXiv:1309.0622 - C.
Andrieu and M. Vihola
“Convergence properties of pseudo-marginal Markov chain Monte Carlo
algorithms,”
*Annals of Applied Probability*, 25(2):1030–1077, 2015. doi:10.1214/14-AAP1022Preprint: arXiv:1210.1484 - C.
Andrieu, V. B. Tadic and M. Vihola
“On the stability of some controlled Markov chains and its applications
to stochastic approximation with Markovian dynamic,”
*Annals of Applied Probability*25(1):1–45, 2015. doi:10.1214/13-AAP953Preprint: arXiv:1205.4181 - B. Miasojedow, E. Moulines and M. Vihola
“An adaptive parallel tempering algorithm,”
*Journal of Computational and Graphical Statistics*22(3):649–664, 2013. doi:10.1080/10618600.2013.778779 - C. Andrieu and M. Vihola
“Markovian stochastic approximation with expanding projections,”
*Bernoulli*, 20(2):545–585, 2014. doi:10.3150/12-BEJ497Preprint: arXiv:1111.5421, PDF - M. Vihola
“Robust adaptive Metropolis algorithm with coerced acceptance
rate,”
*Statistics and Computing*22(5):997–1008, 2012. doi:10.1007/s11222-011-9269-5 - L. Leskelä and M. Vihola
“Stochastic order characterization of uniform integrability and
tightness,”
*Statistics and Probability Letters*83(1):382–389, 2013. doi:10.1016/j.spl.2012.09.023Preprint: arXiv:1106.0607 - M. Vihola
“Can the adaptive Metropolis algorithm collapse without the
covariance lower bound?,”
*Electronic Journal of Probability*16:45–75, 2011. doi:10.1214/EJP.v16-840Preprint: arXiv:0911.0522, PDF - M. Vihola
“On the stability and ergodicity of adaptive scaling Metropolis
algorithms,”
*Stochastic Processes and Their Applications*121(12):2839–2860, 2011. doi:10.1016/j.spa.2011.08.006Preprint: arXiv:0903.4061, PDF - M. Vihola
“Grapham: Graphical models with adaptive random walk Metropolis
algorithms,”
*Computational Statistics & Data Analysis*54(1):49–54, 2010. doi:10.1016/j.csda.2009.09.001 - E. Saksman and M. Vihola
“On the ergodicity of the adaptive
Metropolis algorithm on unbounded domains,”
*Annals of Applied Probability*20(6):2178–2203, 2010. doi:10.1214/10-AAP682Preprint: arXiv:0806.2933, PDF - M. Vihola “Rao-Blackwellised particle filtering in random set
multitarget tracking,”
*IEEE Transactions on Aerospace and Electronic Systems*43(2):689–705, 2007. doi:10.1109/TAES.2007.4285362

# Book chapter

- C. Andrieu, A. Lee and M. Vihola “Theoretical and
methodological aspects of MCMC computations with noisy
likelihoods,”
in S.
A. Sisson, Y. Fan and M. Beaumont (eds.)
*Handbook of Approximate Bayesian Computation*, Chapman and Hall/CRC, 2018.

# In conference proceedings

- F. Lindsten, J. Helske and M. Vihola “Graphical model
inference: Sequential Monte Carlo meets deterministic
approximations,”
*Advances in Neural Information Processing Systems 31*, pp. 8200–8210, 2018.Preprint: arXiv:1901.02374 - M. Vihola “Random set particle filter for bearings-only
multitarget tracking,”
*Signal Processing, Sensor Fusion, and Target Recognition XIV*, ed. Ivan Kadar, Proc. SPIE, Vol. 5809, pp. 301–312, 2005. doi:10.1117/12.602682 - M. Vihola, M.
Harju, P. Salmela, J. Suontausta, and J. Savela.
“Two dissimilarity
measures for HMMs and their application in phoneme model clustering,”
*Proc. ICASSP 2002*, Vol. I, pp. 933–936, 2002. doi:10.1109/ICASSP.2002.5743946

# Theses

- “On the convergence of unconstrained adaptive Markov chain Monte Carlo algorithms,” PhD thesis, University of Jyväskylä, Department of Mathematics and Statistics, Report 124, Jyväskylä 2010, ISSN: 1457-8905, ISBN: 978-951-39-3809-3.
- “Random sets for multitarget tracking and data fusion,” Licentiate thesis, Tampere University of Technology, Oct. 2004.
- “Dissimilarity measures for hidden Markov models and their application in multilingual speech recognition,” MSc thesis, Tampere University of Technology, May 2002.

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